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How can I generate and load dbt manifest properly ...
# ask-ai
y
How can I generate and load dbt manifest properly when running pytest on my Dagster assets?
s
When running pytest on your Dagster assets that integrate with dbt, you'll want to ensure that a dbt manifest is generated and available for your tests. The dbt manifest is a JSON file that contains metadata about your dbt project, including models, tests, sources, and more. Here's how you can generate and load the dbt manifest properly based on the documentation: 1. Generate the dbt manifest: You have two options for generating the dbt manifest: - At runtime: You can generate the 
manifest.json
file at runtime when your Dagster definitions are loaded. This can be done by setting the 
DAGSTER_DBT_PARSE_PROJECT_ON_LOAD
environment variable and running the dbt CLI command to parse your project and create the manifest. Here's an example from the documentation:
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python      import os      from pathlib import Path            from dagster_dbt import DbtCliResource            dbt_project_dir = Path(__file__).joinpath("..", "..", "..").resolve()      dbt = DbtCliResource(project_dir=os.fspath(dbt_project_dir))            if os.getenv("DAGSTER_DBT_PARSE_PROJECT_ON_LOAD"):          dbt_manifest_path = (              dbt.cli(                  ["--quiet", "parse"],                  target_path=Path("target"),              )              .wait()              .target_path.joinpath("manifest.json")          )      else:          dbt_manifest_path = dbt_project_dir.joinpath("target", "manifest.json")
- At build time: Alternatively, you can generate the 
manifest.json
file before running your tests and include it as part of your Python package. This is the recommended approach for production as it avoids the overhead of recompiling your dbt project every time your Dagster code is executed. 2. Load the dbt manifest in your tests: Once you have a 
manifest.json
file, you can use it to define your Dagster assets. The 
@dbt_assets
decorator from the 
dagster_dbt
library can be used to define Dagster assets for dbt models, and it requires the dbt manifest. Here's an example from the documentation:
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python    from dagster import AssetExecutionContext    from dagster_dbt import DbtCliResource, dbt_assets        @dbt_assets(manifest=Path("target", "manifest.json"))    def my_dbt_assets(context: AssetExecutionContext, dbt: DbtCliResource):        # Your dbt asset definitions go here
3. Use pytest fixtures: To integrate this setup with pytest, you can create pytest fixtures that set up the necessary environment variables, generate the dbt manifest, and provide the 
DbtCliResource
instance to your tests. Remember to adjust the paths and commands according to your specific project structure and requirements. The examples provided are based on the documentation and should be adapted to fit your testing setup.
y
@Scout I understand the need to set 
DAGSTER_DBT_PARSE_PROJECT_ON_LOAD=1
when building the manifest, can you go a step deeper how to integrate that to the Pytest Fixture?
s
To integrate the 
DAGSTER_DBT_PARSE_PROJECT_ON_LOAD
environment variable and the dbt manifest generation into a pytest fixture, you can follow these steps: 1. Create a pytest fixture that sets the 
DAGSTER_DBT_PARSE_PROJECT_ON_LOAD
environment variable, generates the dbt manifest, and yields the 
DbtCliResource
instance for use in your tests. 2. Use the 
monkeypatch
fixture provided by pytest to temporarily set the environment variable during the test session. 3. Generate the dbt manifest within the fixture if required, or assume it's already present based on your testing setup. 4. Yield or return the 
DbtCliResource
instance from the fixture for use in your tests. Here's an example of how you might write such a fixture:
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python import os import pytest from pathlib import Path from dagster_dbt import DbtCliResource  @pytest.fixture(scope="session") def dbt_cli_resource(monkeypatch, tmp_path):     # Set the environment variable for the duration of the test session     monkeypatch.setenv("DAGSTER_DBT_PARSE_PROJECT_ON_LOAD", "1")      # Define the dbt project directory (adjust the path as needed)     dbt_project_dir = Path(__file__).parent.resolve()      # Create an instance of DbtCliResource with the project directory     dbt_resource = DbtCliResource(project_dir=os.fspath(dbt_project_dir))      # Generate the dbt manifest if needed     if os.getenv("DAGSTER_DBT_PARSE_PROJECT_ON_LOAD"):         dbt_manifest_path = (             dbt_resource.cli(                 ["--quiet", "parse"],                 target_path=tmp_path,  # Use a temporary directory for the target             )             .wait()             .target_path.joinpath("manifest.json")         )         assert dbt_manifest_path.exists(), "dbt manifest.json was not created"      # Yield the DbtCliResource instance for use in tests     yield dbt_resource      # Any cleanup can be done after the yield statement
In your tests, you can then use the 
dbt_cli_resource
fixture to get an instance of 
DbtCliResource
with the manifest already generated:
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python def test_my_dbt_asset(dbt_cli_resource):     # Use dbt_cli_resource to test your dbt assets     # ...
This fixture will ensure that the 
DAGSTER_DBT_PARSE_PROJECT_ON_LOAD
environment variable is set and that the dbt manifest is generated before any tests that require it are run. The 
tmp_path
fixture provided by pytest creates a temporary directory that is unique to the test function that requests it, ensuring that each test has a clean environment.
y
@Scout I have a different target for my dbt-project, where to specify that for pytest call?
s
If you have a different target for your dbt project, you typically specify this in your dbt profile, which dbt uses to determine the environment and database to run against. However, if you need to specify a different target for your pytest calls, you can do so by passing the 
--target
flag to the dbt CLI commands within your pytest fixtures. Here's how you can modify the pytest fixture to specify a custom target for dbt:
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python import os import pytest from pathlib import Path from dagster_dbt import DbtCliResource  @pytest.fixture(scope="session") def dbt_cli_resource(monkeypatch, tmp_path):     # Set the environment variable for the duration of the test session     monkeypatch.setenv("DAGSTER_DBT_PARSE_PROJECT_ON_LOAD", "1")      # Define the dbt project directory and the target name     dbt_project_dir = Path(__file__).parent.resolve()     dbt_target = "my_custom_target"  # Replace with your target name      # Create an instance of DbtCliResource with the project directory     dbt_resource = DbtCliResource(project_dir=os.fspath(dbt_project_dir))      # Generate the dbt manifest if needed     if os.getenv("DAGSTER_DBT_PARSE_PROJECT_ON_LOAD"):         dbt_manifest_path = (             dbt_resource.cli(                 ["--quiet", "parse", "--target", dbt_target],  # Specify the target here                 target_path=tmp_path,  # Use a temporary directory for the target             )             .wait()             .target_path.joinpath("manifest.json")         )         assert dbt_manifest_path.exists(), "dbt manifest.json was not created"      # Yield the DbtCliResource instance for use in tests     yield dbt_resource      # Any cleanup can be done after the yield statement
In this fixture, the 
dbt_target
variable is set to the name of the custom target you want to use. When calling 
dbt_resource.cli
, the 
--target
flag is included with the 
dbt_target
variable to specify the target for the dbt commands. When you use this fixture in your tests, dbt will run against the specified target, and the generated manifest will reflect that environment. Make sure that the target you specify matches one of the targets defined in your dbt profile so that dbt can correctly connect to the database and run the commands.
y
@Scout all my downstream tests will be using the same fixture, how to set that?
s
If all your downstream tests will be using the same fixture, you can define the fixture once and then import it into each of your test modules, or you can define it in a 
conftest.py
file at the root of your tests directory. Pytest automatically discovers fixtures defined in 
conftest.py
files, making them available to all test files in the directory and subdirectories. Here's how you can set up a 
conftest.py
file: 1. Create a 
conftest.py
file in your tests directory if it doesn't already exist. 2. Define the 
dbt_cli_resource
fixture in `conftest.py`:
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python # conftest.py import os import pytest from pathlib import Path from dagster_dbt import DbtCliResource  @pytest.fixture(scope="session") def dbt_cli_resource(monkeypatch, tmp_path):     # Set the environment variable for the duration of the test session     monkeypatch.setenv("DAGSTER_DBT_PARSE_PROJECT_ON_LOAD", "1")      # Define the dbt project directory and the target name     dbt_project_dir = Path(__file__).parent.resolve()     dbt_target = "my_custom_target"  # Replace with your target name      # Create an instance of DbtCliResource with the project directory     dbt_resource = DbtCliResource(project_dir=os.fspath(dbt_project_dir))      # Generate the dbt manifest if needed     if os.getenv("DAGSTER_DBT_PARSE_PROJECT_ON_LOAD"):         dbt_manifest_path = (             dbt_resource.cli(                 ["--quiet", "parse", "--target", dbt_target],  # Specify the target here                 target_path=tmp_path,  # Use a temporary directory for the target             )             .wait()             .target_path.joinpath("manifest.json")         )         assert dbt_manifest_path.exists(), "dbt manifest.json was not created"      # Yield the DbtCliResource instance for use in tests     yield dbt_resource      # Any cleanup can be done after the yield statement
3. In your test files, simply use the 
dbt_cli_resource
fixture by including it as an argument in your test functions:
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python # test_my_asset.py def test_my_asset(dbt_cli_resource):     # Your test logic here, using dbt_cli_resource     ...
By placing the fixture in 
conftest.py
, you ensure that it's available to all tests without having to import it explicitly. The 
scope="session"
parameter ensures that the fixture is executed only once per test session, which is efficient if the setup is the same for all tests. This approach allows you to maintain a single source of truth for your dbt CLI resource configuration and makes it easy to use across multiple test files.
y
@Scout And how to patch IOmanager or Resources for pytest in a test including a job consisting of multiple assets?
s
To patch an IOManager or Resources for pytest in a test that includes a job consisting of multiple assets, you can use the 
pytest
built-in 
monkeypatch
fixture to override the behavior of these components during the test. This allows you to replace the actual IOManager or Resources with mock or test doubles that have the same interface but contain test-specific logic or assertions. Here's a step-by-step guide on how to do this: 1. Define the mock IOManager or Resource: Create a mock or test double for the IOManager or Resource that you want to patch. This mock should have the same methods and properties as the real IOManager or Resource. 2. Use the 
monkeypatch
fixture: In your test function, use the 
monkeypatch
fixture to replace the real IOManager or Resource with your mock. 3. Run the job: Execute the job within the test function. The job will use the patched IOManager or Resource instead of the real one. 4. Assert the expected behavior: After running the job, assert that the expected behavior occurred, such as checking that certain methods were called on the mock. Here's an example of how you might write a test that patches an IOManager:
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python
import pytest
from dagster import io_manager, build_init_resource_context, AssetMaterialization, Output
from dagster.core.execution.context.init import InitResourceContext
from dagster.core.storage.io_manager import IOManager

# Define a mock IOManager
class MockIOManager(IOManager):
    def handle_output(self, context, obj):
        # Custom logic for testing
        pass

    def load_input(self, context):
        # Custom logic for testing
        return "mocked input"

@pytest.fixture
def mock_io_manager():
    return MockIOManager()

# Use the monkeypatch fixture to patch the IOManager
def test_job_with_mocked_io_manager(monkeypatch, mock_io_manager):
    # Patch the IOManager to use the mock
    monkeypatch.setattr(
        'my_module.my_io_manager_resource',  # Replace with the actual import path
        lambda _: mock_io_manager
    )

    # Define the job and assets as usual
    @io_manager
    def my_io_manager_resource(_):
        return MockIOManager()

    # Run the job
    result = my_job.execute_in_process(
        run_config={
            "resources": {
                "io_manager": {
                    "config": {
                        # Configuration for the mock IOManager if needed
                    }
                }
            }
        }
    )

    # Assert the job ran successfully
    assert result.success

    # Additional assertions can be made here regarding the behavior of the mock IOManager
In this example, 
my_module.my_io_manager_resource
should be replaced with the actual import path of the IOManager you are trying to patch. The `m
y_job` variable should be replaced with the actual job you are testing. By using the 
monkeypatch
fixture, you can control the behavior of the IOManager or Resources during the test without affecting the actual implementation. This is particularly useful for testing edge cases, error handling, or any other behavior that is difficult to trigger under normal conditions.
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