I'm trying to setup Pytest for my Dagster project. However, although all assets definition were loaded successfully using dagster-webserver, but why running pytest, I ran into the error below: "raise DagsterInvalidDefinitionError( E dagster._core.errors.DagsterInvalidDefinitionError: Input asset '["final", "construction_FY_expense_emission"]' for asset '["dhub_construction_cost"]' is not produced by any of the provided asset ops and is not one of the provided sources" The '["final", "construction_FY_expense_emission"]' is a dbt asset.

The error message you're encountering indicates that there is an issue with the asset dependencies in your Dagster project when running tests with Pytest. Specifically, the asset named

dhub_construction_cost

is expecting an input asset named

final

and

construction_FY_expense_emission

, which the system cannot find among the provided assets or sources. This issue could be due to several reasons: 1. The dbt asset

final

and

construction_FY_expense_emission

might not be correctly defined or included in the test environment. Ensure that the dbt asset is correctly configured and that its definition is accessible during the test run. 2. There might be a discrepancy between how assets are loaded in the dagster-webserver and how they are loaded in the test environment. Make sure that the test setup mirrors the actual environment as closely as possible. 3. The asset

dhub_construction_cost

might have been defined with incorrect dependencies. Double-check the asset definitions to ensure that all dependencies are specified correctly. To address this issue, you can follow these steps: - Ensure that the dbt asset is defined and included in your test setup. If you're using a fixture or a setup function to initialize your assets for testing, make sure that the dbt asset is part of that initialization. - If you're using

materialize

in your tests, as shown in the documentation, make sure to include all necessary assets in the list of assets to materialize. For example:

python from dagster import materialize from your_project.assets import dhub_construction_cost, final_construction_FY_expense_emission  def test_my_assets():     assets = [dhub_construction_cost, final_construction_FY_expense_emission]     result = materialize(assets)     assert result.success

- If the dbt asset is an external asset, ensure that it is correctly mocked or represented in the test environment. - Review the asset definitions and dependencies in your Dagster project to ensure they are correctly specified. If you continue to encounter issues, you may need to provide more context or review the specific setup of your dbt assets and how they are integrated into your Dagster project. Remember that testing assets should mimic the actual runtime environment as closely as possible to avoid discrepancies that could lead to errors like the one you're experiencing.

@Scout Here is the asset definition. Can you help me debug it? "# AssetIn takes either key_prefix or key @asset( ins={ "table": AssetIn( key=["final", "construction_FY_expense_emission"], input_manager_key="postgres_replace", ) }, compute_kind="python", group_name="dhub_sync", ) def dhub_construction_cost(table, dhub: ResourceParam[DataHubResource]) -> None: logger.info( f"{len(table)} rows of construction cost data are being synced to DataHub" ) filename = "final_construction_cost.csv" project_id = dhub.get_project_id("Scope3 Construction") logger.info(f"Sync to project: {project_id}!") meta = { "name": filename, "mimeType": "text/csv", "storageContainer": project_id, "destination": "shared-project", "title": "Processed Construction Cost data", "description": "Construction data with expense type and GHG emission", "privacy": "public", "organizations": ["MITOS"], } dhub.sync_dataframe_to_csv(table, meta)"

Based on the asset definition you've provided, it seems that the

dhub_construction_cost

asset is expecting an input asset with the key

["final", "construction_FY_expense_emission"]

. This input asset is expected to be managed by an input manager with the key

postgres_replace

. Here are a few steps to debug the issue: 1. Check Asset Definitions: Ensure that the asset

["final", "construction_FY_expense_emission"]

is defined somewhere in your codebase and that it is accessible in the test environment. If this asset is a dbt asset, make sure that the dbt asset is correctly configured and that its output is available to Dagster. 2. Input Manager: Verify that the input manager with the key

postgres_replace

is correctly configured and available in the test environment. The input manager is responsible for loading the input asset, so if it's not set up correctly, the asset won't be loaded, leading to the error you're seeing. 3. Asset Dependencies: If the asset

["final", "construction_FY_expense_emission"]

is indeed a dbt asset, ensure that the dbt run that produces this asset is executed before the Dagster asset tests are run. This might involve setting up a dbt run within your test setup or mocking the dbt output. 4. Test Environment: Make sure that your test environment includes all necessary configurations for dbt and Dagster to interact correctly. This includes any necessary environment variables, profiles, and target directories. 5. Mocking: If you're writing a unit test and don't want to depend on the actual dbt run, consider mocking the input asset. You can create a fixture that represents the expected output of the dbt asset and use that in your tests. 6. Logging: Use logging within your test to print out information about the assets and input managers being loaded. This can help you identify if the asset is being loaded correctly or if there's an issue with the input manager. 7. Review Error Messages: Look closely at the error messages provided by Pytest. They can often give you clues about what's going wrong. For instance, if the error message indicates that the asset key is not found, it could mean that the asset isn't being included in the test setup. 8. Dagster Documentation: Refer to the Dagster documentation for more information on testing assets and setting up input managers. The documentation provides examples and best practices that can be helpful in debugging issues like this. Remember that when testing Dagster assets, especially those with dependencies on external systems like dbt, it's important to ensure that all dependencies are correctly represented in the test environment. If the issue persists, you may need to provide additional context or review the specific setup of your dbt asse

@Scout thanks. The same asset was loaded fine when running dagster-webserver, and pointing to the dbt_path. How to make sure dbt manifest built with pytest call?

To ensure that the dbt manifest is built when running tests with Pytest, you need to set up your test environment to either generate the dbt manifest at runtime or use a pre-existing manifest file. Here are the steps you can follow based on the documentation: 1. Runtime Manifest Generation: If you want to generate the dbt manifest at runtime during your tests, you can set the

DAGSTER_DBT_PARSE_PROJECT_ON_LOAD

environment variable to

1

. This will instruct Dagster to generate the manifest when it loads the dbt project. You can set this environment variable in your test setup code or as part of your Pytest configuration. 2. Using a Pre-existing Manifest: If you prefer to use a pre-existing dbt manifest (which is recommended for production environments to avoid the overhead of recompiling the dbt project), you should ensure that the manifest file is present in the expected location. The path to the manifest file must be correctly specified in your test setup so that Dagster can locate and use it. 3. Test Fixtures: You can create a Pytest fixture that sets up the dbt environment before your tests run. This fixture can handle setting the environment variable, running the dbt commands to generate the manifest, or loading a pre-existing manifest file. 4. Dbt CLI Resource: If you are using the

DbtCliResource

in your Dagster project, make sure that it is correctly configured in your test environment to point to the correct dbt project directory and profile. Here's an example of how you might set up a Pytest fixture to generate a dbt manifest at runtime:

python
import os
import pytest
from dagster_dbt import dbt_cli_resource

@pytest.fixture(scope="session", autouse=True)
def dbt_manifest():
    os.environ["DAGSTER_DBT_PARSE_PROJECT_ON_LOAD"] = "1"
    dbt_cli = dbt_cli_resource.configured({"project_dir": "path/to/your/dbt/project"})
    dbt_cli.cli(["--quiet", "parse"])
    yield
    # Clean up environment variable after tests
    del os.environ["DAGSTER_DBT_PARSE_PROJECT_ON_LOAD"]

Make sure to replace

"path/to/your/dbt/project"

with the actual path to your dbt project directory. By setting up your test environment correctly and ensuring that the dbt manifest is available, you should be able to run your tests with Pytest without encountering the error related to missing dbt assets. dagster-dbt integration reference button Support dbt-core 1.4.* in dagster-dbt button dagster_dbt requires a default target th... button Using Dagster with dbt, part 2: Load dbt... button Thank you for your feedback!

Thanks, set

DAGSTER_DBT_PARSE_PROJECT_ON_LOAD=1

does solve the issue!