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To write a unit test for a Dagster asset that makes an API call to an external application and returns a DataFrame, you would typically want to follow these steps: 1. Isolate the Asset from External Dependencies: Since unit tests should be fast and not depend on external services, you would mock the API call to the external application. This can be done using Python's built-in

unittest.mock

library or similar mocking libraries. 2. Create a Mock Resource: Define a mock resource that simulates the behavior of the actual resource making the API call. This mock resource should return a predefined DataFrame that you expect from the actual API call. 3. Use the Mock Resource in Your Test: In your unit test, you would use the mock resource instead of the actual resource that makes the API call. This way, you can control the data returned by the API and ensure it is consistent for your test. 4. Invoke the Asset: Use the

build_asset_context

method to construct an

AssetExecutionContext

that includes your mock resource. Then, invoke your asset with this context to test its behavior. 5. Assert the Results: After invoking the asset, you should assert that the output is as expected. This typically involves checking that the DataFrame has the correct structure and contents. Here's an example of what this might look like in code, based on the documentation:

python
from dagster import asset, build_asset_context
from pandas import DataFrame
import pytest
from unittest.mock import Mock

# Assuming you have an asset that makes an API call and returns a DataFrame
@asset(required_resource_keys={"external_api"})
def my_asset(context):
    df = context.resources.external_api.fetch_data()
    return df

# Define a mock resource that returns a predefined DataFrame
class MockExternalAPI:
    def fetch_data(self):
        return DataFrame({"column1": [1, 2, 3], "column2": ["a", "b", "c"]})

# Write a unit test for the asset
def test_my_asset():
    # Create a mock resource
    mock_resource = MockExternalAPI()

    # Build an asset context with the mock resource
    context = build_asset_context(resources={"external_api": mock_resource})

    # Invoke the asset with the mock context
    result = my_asset(context)

    # Assert that the result is as expected
    assert isinstance(result, DataFrame)
    assert result.equals(DataFrame({"column1": [1, 2, 3], "column2": ["a", "b", "c"]}))

In this example,

MockExternalAPI

is a mock version of the resource that would normally make the API call. The

test_my_asset

function uses this mock to provide a controlled environment for the asset to run in, and then asserts that the output is a DataFrame with the expected data. Remember to use

pytest

or your preferred test runner to execute the unit

how to unit test the contents of AssetDefinition object @Scout

Unit testing the contents of an

AssetDefinition

object in Dagster involves verifying that the object has been constructed correctly and that its properties match your expectations. Since an

AssetDefinition

is a representation of an asset within Dagster, you would typically check its configuration, dependencies, and any other relevant properties. Here's a general approach to unit testing an `AssetDefinition`: 1. Create the AssetDefinition: Instantiate the

AssetDefinition

object you want to test. This might be done directly or by invoking a factory function that creates the asset. 2. Inspect the Properties: Access the properties of the

AssetDefinition

object that you want to verify. This could include the asset's name, its input and output definitions, its compute function, and any associated resources or configuration. 3. Write Assertions: Write assertions to check that the properties of the

AssetDefinition

are as expected. This might involve checking that the asset has the correct name, that its dependencies are correctly defined, and that any configuration schemas are as intended. Here's an example of what this might look like in code:

python from dagster import asset, AssetDefinition import pytest  # Define an asset @asset def my_asset():     # Asset logic here     pass  # Function to create an AssetDefinition (this could also be done directly) def create_asset_definition():     return AssetDefinition(         name="my_asset",         compute_fn=my_asset,         # Other properties here     )  # Unit test for the AssetDefinition def test_asset_definition():     asset_def = create_asset_definition()      # Check the name of the asset     assert asset_def.name == "my_asset"      # Check that the compute function is my_asset     assert asset_def.compute_fn == my_asset      # If there are dependencies, configurations, or resources, check those too     # For example:     # assert "some_resource_key" in asset_def.required_resource_keys      # ... additional assertions as needed

In this example,

create_asset_definition

is a hypothetical function that creates an

AssetDefinition

object. The

test_asset_definition

function then checks various properties of the

AssetDefinition

to ensure they are as expected. Remember that the specifics of how you test an

AssetDefinition

will depend on the complexity of your asset and what properties you need to verify. The example above is a simple case; more complex assets might require more detailed testing, including checking config schemas, resource requirements, and input/output definitions.

I have a function that returns an asset and I want to unit test that function. Following an asset factory approach here.

def get_org_datasources(organisation_id: int, customer_code_with_suffix : str, armada_api_client : armada_client.ArmadaClient):

    @asset(
        name=,
        description=,
        io_manager_key=,
        group_name=
    )
    def _asset(context):

        return Output(ds_df)

    return _asset

@Scout

To unit test a function that returns an

AssetDefinition

using an asset factory approach, you will want to verify that the function correctly constructs the asset with the expected properties. Here's how you can approach this: 1. Invoke the Asset Factory Function: Call the function that returns the

AssetDefinition

with the appropriate parameters. 2. Inspect the Returned Asset: Check the properties of the returned asset to ensure they match your expectations. This includes the asset's name, description, I/O manager key, group name, and any other relevant properties. 3. Write Assertions: Write assertions to verify that the asset has been constructed correctly. Here's an example of what the unit test might look like:

python
from dagster import build_asset_context
import pytest
import armada_client

# Assuming you have a function that returns an asset like the one you provided
def get_org_datasources(organisation_id: int, customer_code_with_suffix: str, armada_api_client: armada_client.ArmadaClient):
    # ... your existing implementation ...

# Unit test for the asset factory function
def test_get_org_datasources():
    # Mock the ArmadaClient or use a real instance if appropriate
    mock_armada_api_client = armada_client.ArmadaClient()

    # Call the factory function to get the asset
    org_datasources_asset = get_org_datasources(123, "customer_001", mock_armada_api_client)

    # Now, org_datasources_asset is an AssetDefinition object.
    # You can inspect its properties and write assertions.

    # Check the name of the asset (you need to define what you expect here)
    assert org_datasources_asset.name == "expected_name"

    # Check the description of the asset (you need to define what you expect here)
    assert org_datasources_asset.description == "expected_description"

    # Check the I/O manager key of the asset (you need to define what you expect here)
    assert org_datasources_asset.io_manager_key == "expected_io_manager_key"

    # Check the group name of the asset (you need to define what you expect here)
    assert org_datasources_asset.group_name == "expected_group_name"

    # If you want to test the compute function, you can build an asset context and invoke it
    context = build_asset_context()
    result = org_datasources_asset.compute_fn(context)

    # Perform assertions on the result of the compute function
    # For example, check the type and contents of the result if it's a DataFrame
    assert isinstance(result, Output)
    # ... additional assertions about the contents of the DataFrame ...

In this example, you would replace

"expected_name"

,

"expected_description"

,

"expected_io_manager_key"

, and

"expected_group_name"

with the actual values you expect based on the inputs to the `get_or