In the Dagster UI is it possible to choose

in_process

executor by manually selecting assets to run?

Yes, in the Dagster UI, you can choose the

in_process

executor by configuring the run for your assets. When you use the Launchpad in the Dagster UI to execute a job, you have the option to interactively build up the configuration for the run. This includes setting the

executor

configuration to

in_process

, which means that the compute steps will execute in a single process as opposed to the default multi-process executor. This can be helpful for jobs with short-running compute steps where the overhead of launching subprocesses for each step is non-trivial compared to the execution time of the steps themselves. To do this, you would provide the appropriate run config in the configuration editor within the Launchpad tab of the Dagster UI. The documentation provides an example of how to specify the

in_process

executor in the run config as YAML, which you would use in the Dagster UI playground to launch an in-process execution.

@Scout Launchpad is just for jobs, how can I do this by selecting assets to run

As of my knowledge cutoff in early 2023, the Dagster UI's Launchpad is primarily designed for launching jobs, which can include asset jobs. When you want to run specific assets, you typically use the asset graph in the UI to select and materialize assets. However, the UI may not provide a direct way to specify the

in_process

executor when manually selecting assets to run. The

in_process

executor is typically specified in the job's run configuration, and when you materialize assets directly through the UI, you're not interacting with a job's run configuration in the same way. If you need to run specific assets with the

in_process

executor, you might consider defining an asset job with the desired executor configuration and then selecting the assets you want to include in that job. If there have been updates to the Dagster UI that allow for more granular control over the executor when materializing assets directly, you would find that information in the latest Dagster documentation or release notes. Since the UI and capabilities of Dagster can evolve, it's always a good idea to check the latest documentation or the Dagster GitHub repository for any new features or changes that might affect how you can run assets.

@Scout Ok how to change this to have it run with in_process: reconciliation_train_data_job = define_asset_job( name="reconciliation_train_data", selection=( AssetSelection.assets(reconciliation_labels).upstream() | AssetSelection.assets(upload_embedding).upstream() ), )

To run the

reconciliation_train_data_job

with the

in_process

executor, you would need to modify the job definition to include the

executor_def

parameter, specifying the

in_process_executor

. Here's how you can adjust your job definition:

python from dagster import in_process_executor, define_asset_job, AssetSelection  # Assuming reconciliation_labels and upload_embedding are already defined assets reconciliation_train_data_job = define_asset_job(     name="reconciliation_train_data",     selection=(         AssetSelection.assets(reconciliation_labels).upstream()         | AssetSelection.assets(upload_embedding).upstream()     ),     executor_def=in_process_executor, )

By adding

executor_def=in_process_executor

, you are instructing Dagster to use the

in_process

executor when this job is executed. This means that all the steps of the job will run in a single process. Please note that the

in_process_executor

is suitable for certain scenarios, such as when you have lightweight computations or when you want to avoid the overhead of inter-process communication. However, it may not be appropriate for all workloads, especially those that are CPU-bound or require isolation between steps. Remember to import the

in_process_executor

from Dagster's API to use it in your job definition. If you encounter any issues or if the API has changed, please refer to the latest Dagster documentation for the most up-to-date information on defining jobs and executors.

@Scout How can I write code to test specifically a single asset and simulating the input dependencies

To test a single asset and simulate its input dependencies, you can directly invoke the asset's computation function with mock inputs that represent the dependencies. This allows you to test the asset in isolation without having to materialize the entire upstream graph. Here's a step-by-step guide on how to write a test for an individual asset: 1. Identify the asset you want to test and its upstream dependencies. 2. Create mock data or objects that represent the expected outputs of the upstream dependencies. 3. Invoke the asset's computation function directly in your test, passing in the mock data as arguments. 4. Assert the expected behavior or output of the asset's computation function. Here's an example of how you might write a test for an asset:

python import pytest from dagster import asset  # Example of an upstream asset @asset def upstream_asset():     # This would normally compute something     pass  # Example of the asset you want to test, which depends on `upstream_asset` @asset def asset_to_test(upstream_asset):     # This asset does something with the data from `upstream_asset`     return upstream_asset + 1  # Test function for `asset_to_test` def test_asset_to_test():     # Create mock data for `upstream_asset`     mock_upstream_data = 1  # Replace with appropriate mock data      # Call `asset_to_test` directly with mock data     result = asset_to_test(mock_upstream_data)      # Assert expected result     assert result == 2  # Replace with the expected result of the asset computation

In this example,

upstream_asset

is a simple asset that we're mocking with a constant value. In a real test, you would replace

mock_upstream_data

with a more realistic mock that represents the output of the

upstream_asset

. If your asset requires more complex setup or has side effects, you may need to use fixtures or mocks to properly isolate and test the asset's behavior. The

pytest

framework provides powerful tools for this purpose, such as

pytest.fixture

for setting up test data and

unittest.mock

for replacing parts of your system under test with mock objects. Remember to check the latest Dagster documentation for any updates or changes to the testing APIs and best practices.

@Scout how to change the default executor to in_process?

To change the default executor to

in_process

for a Dagster instance, you can set the default executor in your Dagster repository definition. This will apply the

in_process

executor to all jobs and asset jobs within the repository unless they explicitly override the executor. Here's an example of how to set the default executor to

in_process

in your repository definition:

python from dagster import repository, in_process_executor from dagster._core.definitions.executor_definition import default_executors  # Import your jobs and assets here # from my_jobs import my_job # from my_assets import my_asset_group  @repository def my_repository():     return [         # Add your jobs and assets here         # my_job,         # my_asset_group,         # Add default executors with in_process_executor included         *default_executors + [in_process_executor],     ]

By including

in_process_executor

in the list of default executors, you're specifying that the

in_process

executor should be available as a default option for jobs in your repository. When you launch a job or materialize assets, Dagster will use the

in_process

executor by default. However, if you have a specific job that requires a different executor, you can override the default executor in the job definition by specifying the

executor_def

parameter. Please note that the code snippet above assumes that you are using the repository decorator to define your repository. If you are using a different method to define your repository, you may need to adjust the code accordingly. Always refer to the latest Dagster documentation for the most up-to-date guidance on configuring executors and repositories.

@Scout considering this:

python
import pytest
from dagster import asset

# Example of an upstream asset
@asset
def upstream_asset():
    # This would normally compute something
    pass

# Example of the asset you want to test, which depends on `upstream_asset`
@asset
def asset_to_test(upstream_asset):
    # This asset does something with the data from `upstream_asset`
    return upstream_asset + 1

# Test function for `asset_to_test`
def test_asset_to_test():
    # Create mock data for `upstream_asset`
    mock_upstream_data = 1  # Replace with appropriate mock data

    # Call `asset_to_test` directly with mock data
    result = asset_to_test(mock_upstream_data)

    # Assert expected result
    assert result == 2  # Replace with the expected result of the asset computation

How can I run the test? Also how can I place this in the

data_pipeline_test

directory?

To run the test for your asset, you would typically use a test runner like

pytest

. Here's how you can organize your tests in a

data_pipeline_tests

directory and run them: 1. Create a directory structure for your tests. For example:

my_dagster_project/ ├── data_pipeline/ │   ├── assets.py └── data_pipeline_tests/     ├── test_assets.py

2. Place your asset definitions in

assets.py

within the

data_pipeline

directory. 3. Create a

test_assets.py

file within the

data_pipeline_tests

directory. This file will contain your test code. 4. Write your test code in

test_assets.py

. Here's an example based on the code you provided:

python # data_pipeline_tests/test_assets.py  import pytest from data_pipeline.assets import asset_to_test  # Test function for `asset_to_test` def test_asset_to_test():     # Create mock data for `upstream_asset`     mock_upstream_data = 1  # Replace with appropriate mock data      # Call `asset_to_test` directly with mock data     result = asset_to_test(mock_upstream_data)      # Assert expected result     assert result == 2  # Replace with the expected result of the asset computation

5. To run the test, open a terminal and navigate to the root directory of your Dagster project (where

my_dagster_project

is the root directory in this example). 6. Execute the

pytest

command, specifying the path to your tests:

bash pytest data_pipeline_tests/

pytest

will discover and run all test functions defined in the

data_pipeline_tests

directory that start with

test_

. Make sure you have

pytest

installed in your environment. If not, you can install it using `pip`:

bash pip install pytest

This is a simplified example. Depending on the complexity of your assets and their dependencies, you might need to use fixtures or mocks to properly set up your test environment. The

pytest

framework provides powerful tools for setting up and tearing down test data, as well as for mocking external dependencies. Remember to import your assets correctly in the test file. In the example above, it's assumed that the

asset_to_test

function is defined in the

data_pipeline/assets.py

file, and we're importing it into the

test_assets.py

file for testing. Adjust the import statement according to your actual module and asset names.

@Scout what if I want to do this but to import an asset from

data_pipeline

inside

data_pipeline_test

?

To import an asset from

data_pipeline

inside

data_pipeline_tests

, you need to ensure that Python can locate your

data_pipeline

module. Here's how you can structure your project and import the asset for testing: 1. Ensure your project structure looks something like this:

my_dagster_project/ ├── data_pipeline/ │   ├── __init__.py    # This makes data_pipeline a Python package │   └── assets.py └── data_pipeline_tests/     ├── __init__.py    # This makes data_pipeline_tests a Python package     └── test_assets.py

The

__init__.py

files can be empty but are necessary for Python to recognize the directories as packages. 2. Inside your

test_assets.py

, import the asset from the

data_pipeline

package. Here's an example:

python # data_pipeline_tests/test_assets.py  import pytest from data_pipeline.assets import asset_to_test  # Test function for `asset_to_test` def test_asset_to_test():     # Create mock data for `upstream_asset`     mock_upstream_data = 1  # Replace with appropriate mock data      # Call `asset_to_test` directly with mock data     result = asset_to_test(mock_upstream_data)      # Assert expected result     assert result == 2  # Replace with the expected result of the asset computation

3. Run the tests using

pytest

from the root directory of your project:

bash pytest data_pipeline_tests/

If you encounter an

ImportError

, it may be due to Python not recognizing your project's root directory as a package. To resolve this, you can add an empty

__init__.py

file to the root directory or modify the

PYTHONPATH

environment variable to include the root directory of your project. Alternatively, you can use a

pytest.ini

or

pyproject.toml

file to configure the test paths for

pytest

, or you can run

pytest

with the

-m

flag to specify the Python path:

bash PYTHONPATH=. pytest data_pipeline_tests/

This tells Python to include the current directory (the root of your project) in the Python module search path. Remember to adjust the import paths according to your actual module and asset names. The documentation provides guidance on how to write unit tests for individual assets, and you can follow similar steps to set up your test environment and run your tests.