@Scout How do I write a test to confirm the version returned by a @observable_source_asset?

To write a test to confirm the version returned by an

@observable_source_asset

, you would typically define the observable source asset with an observation function that returns a

DataVersion

. Then, in your test, you would invoke the observation function and assert that the returned

DataVersion

matches your expectations. Here's a step-by-step guide based on the documentation: 1. Define your

@observable_source_asset

with an observation function that returns a

DataVersion

. This function should perform the necessary logic to determine the data version of your source asset. 2. Write a test function where you call the observation function of your observable source asset directly. 3. In the test function, capture the returned

DataVersion

from the observation function. 4. Assert that the captured

DataVersion

matches the expected data version. The documentation provides an example of defining an observable source asset:

python from hashlib import sha256 from dagster import DataVersion, observable_source_asset  @observable_source_asset def foo_source_asset():     content = read_some_file()     hash_sig = sha256()     hash_sig.update(bytearray(content, "utf8"))     return DataVersion(hash_sig.hexdigest())

In this example, the

foo_source_asset

function reads the content of a file, computes its SHA256 hash, and returns that hash as a

DataVersion

. To test this asset, you would write a test function similar to the following:

python def test_foo_source_asset():     expected_version = "expected_hash_here"  # Replace with the expected hash     actual_version = foo_source_asset().hexdigest()     assert actual_version == expected_version

In the test function

test_foo_source_asset

, you would replace

"expected_hash_here"

with the actual expected hash value for the content of the file being read. Then, you call the

foo_source_asset

function, get the hexdigest of the returned

DataVersion

, and assert that it equals the expected version. Please note that the actual implementation of your test may vary depending on the specifics of your observable source asset and the logic you use to determine the data version. The provided example is a simplified version based on the documentation's example.

@Scout TypeError: 'SourceAsset' object is not callable

I apologize for the confusion. The example I provided was incorrect. In Dagster,

@observable_source_asset

is used to define an observable source asset, and it is not directly callable like a regular function. Instead, you would typically interact with the asset within the context of a Dagster job or graph. To test an observable source asset, you would need to set up a test that runs the job or graph that includes the observable source asset and then check the materialization event to confirm that the correct version is being returned. Here's a more appropriate way to test an observable source asset: 1. Define your observable source asset with a function that returns a

DataVersion

. 2. Define a job that includes this observable source asset. 3. In your test, run the job using

execute_in_process

or a similar method that allows you to capture events. 4. Inspect the materialization events to confirm that the

DataVersion

associated with the observable source asset is as expected. Here's a conceptual example:

python from dagster import observable_source_asset, AssetMaterialization, execute_in_process  @observable_source_asset def my_observable_source_asset():     # Logic to determine the data version     version = "some_version_string"     yield AssetMaterialization(asset_key="my_asset", metadata={"version": version})     yield DataVersion(version)  def test_my_observable_source_asset():     result = execute_in_process(asset_key="my_asset")     materializations = result.events_for_asset_key("my_asset", event_type=AssetMaterialization)          assert len(materializations) == 1     materialization = materializations[0]     assert materialization.dagster_event.event_specific_data.metadata_entries[0].entry_data.text == "some_version_string"

In this test,

execute_in_process

is used to run the asset within a test environment, and then we filter the result for

AssetMaterialization

events related to our asset. We then assert that the version in the materialization metadata matches the expected version. Please adjust the example to fit the specifics of your observable source asset and the actual logic you use to determine the data version. The provided example is a conceptual guide and may require modifications to work with your actual codebase.

@Scout What is

execute_in_process

? Cant import from Dagster

execute_in_process

is a method provided by Dagster for executing jobs or graphs in-process, which means that the execution happens within the same Python process that calls the method. This is particularly useful for testing because it allows you to run your Dagster code and inspect the results without having to set up an external Dagster instance or use the Dagster CLI. When you use

execute_in_process

, Dagster will execute the job or graph and return an

ExecuteInProcessResult

object, which contains information about the run, including events, outputs, and whether the run was successful. To use

execute_in_process

, you need to import it from the

dagster

package. Here's how you can do it:

python from dagster import job, op, execute_in_process  @op def my_op():     return "Hello, Dagster!"  @job def my_job():     my_op()  def test_my_job():     result = my_job.execute_in_process()     assert result.success

In this example,

my_job

is a simple Dagster job that includes a single operation

my_op

. The

test_my_job

function demonstrates how to execute

my_job

in-process and assert that the execution was successful. If you're having trouble importing

execute_in_process

, make sure that you have the correct version of Dagster installed, as the API can change between versions. You can install or upgrade Dagster using pip:

bash pip install dagster

Or, if you need to upgrade:

bash pip install --upgrade dagster

If you continue to have issues, please check the Dagster release notes or documentation for any changes to the API or the correct import paths.

@Scout I don't think that

execute_in_process

is a standalone function

You are correct; my apologies for the confusion.

execute_in_process

is not a standalone function. It is a method on the

JobDefinition

class in Dagster, which means it is used in the context of a job object. Here's how you can use

execute_in_process

correctly:

python from dagster import job, op  @op def my_op():     return "Hello, Dagster!"  @job def my_job():     my_op()  def test_my_job():     result = my_job.execute_in_process()     assert result.success

In this example,

my_job

is a Dagster job that includes the operation

my_op

. The

test_my_job

function demonstrates how to execute

my_job

in-process by calling the

execute_in_process

method on the

my_job

object. The result of the execution is an

ExecuteInProcessResult

object, which can be used to assert the success of the run and inspect other aspects of the execution. If you are writing tests for your Dagster jobs, using

execute_in_process

is a convenient way to execute your jobs synchronously in the same process and verify their behavior.

@Scout Mock a job

In Dagster, mocking a job for testing purposes typically involves mocking the individual assets or ops that make up the job. By mocking these components, you can isolate the behavior you want to test and avoid executing any external dependencies or side effects. Here's a conceptual example of how you might mock an asset within a job:

python from dagster import asset, build_assets_job, AssetIn, AssetOut, AssetsDefinition from unittest.mock import patch  # Define your assets @asset def my_asset():     # Some logic that you want to mock in your tests     return "real_data"  # Define a job that includes your asset my_job = build_assets_job("my_job", assets=[my_asset])  # Write a test function where you mock the asset def test_my_job_with_mocked_asset():     with patch("path.to.my_asset_module.my_asset") as mock_my_asset:         # Configure the mock to return the value you want         mock_my_asset.return_value = "mocked_data"                  # Execute the job in process         result = my_job.execute_in_process()          # Assert that the job succeeded         assert result.success          # Optionally, assert that your mock was called (if relevant)         mock_my_asset.assert_called_once()

In this example, we use Python's

unittest.mock.patch

to replace the

my_asset

function with a mock that returns "mocked_data" when called. We then execute the job in-process and assert that the job succeeded. Optionally, we can also assert that our mock was called exactly once. Please note that the path provided to

patch

should be the full import path to the asset you are mocking. This means if

my_asset

is defined in a module named

my_assets_module

, the path would be

"path.to.my_assets_module.my_asset"

. This approach allows you to test the behavior of your job under controlled conditions, without relying on the actual implementation of the assets that may have external dependencies or side effects.

@Scout Mock a job for the purposes of testing a sensor

To mock a job for the purposes of testing a sensor in Dagster, you can use the

build_sensor_context

function to create a context for the sensor evaluation. This allows you to simulate the sensor's execution environment and test its behavior without actually running the job. Here's a conceptual example of how you might test a sensor that triggers a job:

python from dagster import sensor, RunRequest, build_sensor_context, job from unittest.mock import patch  # Define your job @job def my_job():     # Job definition     pass  # Define your sensor @sensor(job=my_job) def my_sensor(context):     # Sensor logic that decides when to trigger the job     if some_condition:         yield RunRequest(run_key="some_run_key", run_config={})  # Write a test function where you mock the job and test the sensor def test_my_sensor():     # Mock the job execution to prevent it from actually running     with patch("path.to.my_job_module.my_job") as mock_my_job:         # Configure the mock as needed, e.g., to assert it was called         mock_my_job.return_value = None          # Build a sensor context for testing         context = build_sensor_context()          # Execute the sensor         sensor_gen = my_sensor(context)         sensor_result = list(sensor_gen)  # Evaluate the generator          # Assert that a RunRequest was yielded by the sensor         assert len(sensor_result) == 1         assert isinstance(sensor_result[0], RunRequest)          # Optionally, assert that the job was triggered with the correct run key         assert sensor_result[0].run_key == "some_run_key"          # If your sensor logic involves the job execution, you can assert that as well         # mock_my_job.assert_called_once()

In this example, we define a job

my_job

and a sensor

my_sensor

that triggers the job based on some condition. In the test function

test_my_sensor

, we use

unittest.mock.patch

to mock the job to prevent it from actually running when the sensor triggers it. We then use

build_sensor_context

to create a context for the sensor evaluation and execute the sensor. We convert the generator returned by the sensor to a list to evaluate all items it yields. We assert that a

RunRequest

was yielded by the sensor and optionally check the

run_key

or other properties of the

RunRequest

. Please adjust the example to fit the specifics of your sensor and job. The provided example is a conceptual guide and may require modifications to work with your actual codebase.