@Scout if i'm using

build_sensor_context

in a test, how do i use it along with a mock

DagsterRun

instance?

When using

build_sensor_context

in a test, you can create a mock

DagsterRun

instance and pass it to the context builder function to simulate a run context for your sensor. However, the documentation does not provide a direct example of using

build_sensor_context

with a mock

DagsterRun

instance. Instead, it provides guidance on how to build a sensor execution context using the provided parameters, which can be used to invoke a sensor definition. Here's a general approach based on the documentation: 1. Create an ephemeral Dagster instance using

DagsterInstance.ephemeral()

. 2. Execute a job or an operation to generate a

DagsterRun

object. 3. Use the

DagsterRun

object to build the sensor context. Here is a code snippet from the documentation that demonstrates how to build the context for a run status sensor, which you can adapt for your use case with a mock `DagsterRun`:

python from dagster import DagsterInstance, build_run_status_sensor_context  # Create an ephemeral Dagster instance instance = DagsterInstance.ephemeral()  # Execute a job to generate a DagsterRun object (this is where you would use your mock instead) result = my_job.execute_in_process(instance=instance) dagster_run = result.dagster_run  # Create the sensor context sensor_context = build_run_status_sensor_context(     sensor_name="my_sensor",     dagster_instance=instance,     dagster_run=dagster_run,     dagster_event=result.get_job_success_event(),  # or get_job_failure_event() )  # Now you can invoke your sensor with the created context my_sensor(sensor_context)

In this example,

my_job

would be a job that you expect to succeed and generate a

DagsterRun

object. If you want to use a mock

DagsterRun

, you would replace the execution of

my_job

with the creation of a mock

DagsterRun

object that suits your testing needs. Remember that the

build_sensor_context

function is used to build a context for sensor invocation, and it can accept parameters such as

instance

,

cursor

,

repository_name

,

repository_def

,

resources

, and

definitions

. If you need to mock the

DagsterRun

for an asset sensor, you would need to provide an instance parameter when building your context, as asset sensors rely on querying for new events from the

DagsterInstance

. Please adapt the above example to fit your specific testing scenario, and if you need to mock specific behaviors or attributes of the

DagsterRun

, you can do so using standard Python mocking libraries like

unittest.mock

.

@Scout here's an excerpt from my current test setup:

self.run_id = "abc123"
        self.tags = {
            "job_completion_callback_url": "<https://www.foo.com>",
            "job_completion_callback_auth": "auth_token"
        }
        self.mock_dagster_instance = DagsterInstance.ephemeral()
        self.job_name = "some_job"
        self.mock_dagster_run = DagsterRun(run_id=self.run_id, tags=self.tags, job_name=self.job_name)

        result = self.job_name.execute_in_process(instance=self.mock_dagster_instance)
        dagster_run = result.mock_dagster_run

        # Create the sensor context
        self.sensor_context = build_run_status_sensor_context(
            sensor_name="my_sensor",
            dagster_instance=self.mock_dagster_instance,
            dagster_run=dagster_run,
            dagster_event=result.get_job_success_event(),  # or get_job_failure_event()
        )

could you help me fix the following error i get when running my test suite?

AttributeError: 'str' object has no attribute 'execute_in_process'

The error you're encountering,

AttributeError: 'str' object has no attribute 'execute_in_process'

, is occurring because

self.job_name

is a string and not a job object. The

execute_in_process

method is a method of a job object, not a string. To fix this error, you need to ensure that

self.job_name

is assigned to an actual job object that you've defined using the

@job

decorator. Here's an example of how you might define a job:

python from dagster import job, op  @op def my_op():     # Your operation logic here     pass  @job def some_job():     my_op()

Once you have defined your job, you can reference it when setting `self.job_name`:

python self.job_name = some_job

Now, when you call

execute_in_process

on

self.job_name

, it will be calling the method on the job object, not a string:

python result = self.job_name.execute_in_process(instance=self.mock_dagster_instance)

Make sure to replace

some_job

with the actual job object you have defined in your code. Here's how your test setup should look after the correction:

python from dagster import DagsterInstance, build_run_status_sensor_context, DagsterRun  # Assuming some_job is a job object you have defined elsewhere in your code self.run_id = "abc123" self.tags = {     "job_completion_callback_url": "<https://www.foo.com>",     "job_completion_callback_auth": "auth_token" } self.mock_dagster_instance = DagsterInstance.ephemeral() self.job_name = some_job  # Replace with your actual job object self.mock_dagster_run = DagsterRun(run_id=self.run_id, tags=self.tags, job_name=self.job_name.name)  result = self.job_name.execute_in_process(instance=self.mock_dagster_instance) dagster_run = result.dagster_run  # Use the result's dagster_run, not mock_dagster_run  # Create the sensor context self.sensor_context = build_run_status_sensor_context(     sensor_name="my_sensor",     dagster_instance=self.mock_dagster_instance,     dagster_run=dagster_run,     dagster_event=result.get_job_success_event(),  # or get_job_failure_event() )

Make sure that

some_job

is the job you want to test, and replace

my_sensor

with the actual sensor name you are testing. This should resolve the

AttributeError

you're encountering.

@Scout i don't use the @job decorator when defining my job; i instead do so as follows:

all_assets = load_assets_from_modules([assets], group_name=GROUP_NAME)

materialize_all_assets_job = define_asset_job(
    name="some_job",
    selection=AssetSelection.all(),
    # auto-terminate run after 20 minutes; we can toggle this up or down as needed
    tags={MAX_RUNTIME_SECONDS_TAG: 1200},
)

defs = Definitions(
    assets=all_assets, 
    jobs=[materialize_all_assets_job], 
    sensors=[success_callback, failure_callback]
)

how should i therefore fix my test setup?

Given that you're using

define_asset_job

to create your job, you'll need to resolve the job from the

Definitions

object before you can execute it in process. The

define_asset_job

function returns an

UnresolvedAssetJobDefinition

, which needs to be resolved with the actual asset definitions before it can be executed. Here's how you can adjust your test setup: 1. Use the

Definitions

object to get the resolved job definition. 2. Execute the resolved job in process. 3. Use the result of the execution to create the sensor context. Here's an example of how you might adjust your test setup:

python from dagster import DagsterInstance, build_run_status_sensor_context, DagsterRun, Definitions, define_asset_job, AssetSelection  # Define your assets and job as you have in your actual code all_assets = load_assets_from_modules([assets], group_name=GROUP_NAME)  materialize_all_assets_job = define_asset_job(     name="some_job",     selection=AssetSelection.all(),     tags={MAX_RUNTIME_SECONDS_TAG: 1200}, )  # Create a Definitions object with your assets and job defs = Definitions(     assets=all_assets,      jobs=[materialize_all_assets_job],      sensors=[success_callback, failure_callback] )  # Resolve the job from the Definitions object resolved_job = defs.get_job_def("some_job")  # Create an ephemeral Dagster instance mock_dagster_instance = DagsterInstance.ephemeral()  # Execute the resolved job in process result = resolved_job.execute_in_process(instance=mock_dagster_instance) dagster_run = result.dagster_run  # Create the sensor context sensor_context = build_run_status_sensor_context(     sensor_name="my_sensor",     dagster_instance=mock_dagster_instance,     dagster_run=dagster_run,     dagster_event=result.get_job_success_event(),  # or get_job_failure_event() )

In this setup,

resolved_job

is the job definition that you can execute in process. The

execute_in_process

method will return a result object that contains the

dagster_run

and the events that you can use to create the sensor context. Make sure to replace

success_callback

,

failure_callback

,

assets

, and

GROUP_NAME

with the actual values from your code. This should allow you to execute your job in the test and create the sensor context without encountering the

AttributeError

.

why is this so complicated?