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#ask-ai
Title
# ask-ai
b

BC A

02/09/2024, 4:59 PM
how to build a run sucess sensor to trigger a job located in another location
s

Scout

02/09/2024, 4:59 PM
To build a run success sensor that triggers a job located in another code location, you can use the
CodeLocationSelector
and
JobSelector
identifiers provided by Dagster. Here's a step-by-step guide based on the documentation: 1. Define the Sensor: Use the
@run_status_sensor
decorator to define your sensor function. This decorator allows you to specify the run status that the sensor should react to. 2. Specify Monitored Jobs: Use the
monitored_jobs
parameter to specify which jobs the sensor should monitor. If the job you want to monitor is in a different code location, you can use
CodeLocationSelector
to specify the location of the job. 3. Trigger Another Job: To trigger a job in another code location when the monitored job succeeds, you can use the
JobSelector
to specify the job you want to trigger. Here's an example from the documentation:
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python from dagster import run_status_sensor, DagsterRunStatus, CodeLocationSelector, JobSelector  @run_status_sensor(     monitored_jobs=[CodeLocationSelector(location_name="defs")],     run_status=DagsterRunStatus.SUCCESS, ) def code_location_a_sensor():     # when any job in code_location_a succeeds, this sensor will trigger     send_slack_alert()  @run_failure_sensor(     monitored_jobs=[         JobSelector(             location_name="defs",             repository_name="code_location_a",             job_name="data_update",         )     ], ) def code_location_a_data_update_failure_sensor():     # when the data_update job in code_location_a fails, this sensor will trigger     send_slack_alert()
In the example above,
code_location_a_sensor
is a run status sensor that triggers when any job in the code location named "defs" succeeds. The
code_location_a_data_update_failure_sensor
is a run failure sensor that triggers when the "data_update" job in the "defs" code location fails. To trigger another job, you would need to return a
RunRequest
from your sensor function. The
RunRequest
should specify the job to be triggered and any necessary run configuration. Please note that the example provided does not include the triggering of another job, but you can adapt the sensor function to return a
RunRequest
for the job you want to trigger in another code location. Remember to replace
send_slack_alert()
with the logic to trigger the job in another location, which would typically involve returning a
RunRequest
object with the appropriate job and configuration.
b

BC A

02/09/2024, 5:57 PM
@Scout I want to trigger a job in the other code location inside the success run status sensor
s

Scout

02/09/2024, 5:57 PM
To trigger a job in another code location from within a run success sensor, you would typically define a sensor that monitors for successful runs and then issues a
RunRequest
for the job you want to trigger. However, the
@run_status_sensor
decorator does not directly support specifying a
request_job
parameter for jobs in other code locations. Instead, you can use the
JobSelector
within your sensor function to identify the job you want to trigger and then construct a
RunRequest
with the necessary configuration. Here's a conceptual example based on the documentation:
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python from dagster import run_status_sensor, DagsterRunStatus, RunRequest, JobSelector  @run_status_sensor(     monitored_jobs=[CodeLocationSelector(location_name="source_location")],     run_status=DagsterRunStatus.SUCCESS, ) def trigger_job_in_other_location(context):     # Check if the job that succeeded is the one you want to monitor     if context.dagster_run.job_name == "monitored_job_name":         # Define the job you want to trigger in the other code location         job_to_trigger = JobSelector(             location_name="target_location",             repository_name="target_repository",             job_name="target_job_name",         )                  # Define the run configuration for the job you want to trigger         run_config = {             # ... your run configuration goes here ...         }                  # Return a RunRequest to trigger the job         return RunRequest(             run_key=context.dagster_run.run_id,  # or some other unique identifier             run_config=run_config,             tags={"triggered_by": "success_sensor"},  # optional tags             job_name=job_to_trigger.job_name,  # specify the job to trigger         )
In this example, the sensor is monitoring for successful runs of a job in the "source_location" code location. When a run of "monitored_job_name" succeeds, the sensor constructs a
RunRequest
for "target_job_name" in the "target_location" code location with the specified
run_config
. Please note that the
JobSelector
is used here conceptually to identify the job you want to trigger. In practice, you would need to ensure that the
RunRequest
is correctly configured to trigger the job in the other code location, which may involve additional setup or context depending on your specific Dagster deployment and configuration. If you need to trigger a job in another code location and the above approach does not fit your use case, please consult the Dagster documentation or community for more specific guidance, as the exact mechanism may vary with different versions or configurations of Dagster.
b

BC A

02/10/2024, 6:08 PM
@Scout Defined my sensor as such and it's not working : @run_status_sensor( monitored_jobs=[elt_pipeline_weekly_job], run_status=DagsterRunStatus.SUCCESS, ) def riRunDataPrepSensor(context: RunStatusSensorContext): dataprep_job = JobSelector( location_name="dataprep-repo", repository_name="dataprep_repo", job_name="k8s_dataprep_RI_partitioned" ) run_config = { "bucket":"dd-dd-dd", "org_id":"dd", "region":"dd", "bq_dataset":"dd" } return RunRequest( run_key=context.dagster_run.run_id, run_config=run_config, tags=context.dagster_run.tags, job_name=dataprep_job.job_name )
s

Scout

02/10/2024, 6:08 PM
Based on the code snippet you've provided, it seems you are trying to create a
RunRequest
for a job in another code location when a monitored job succeeds. However, there are a few potential issues that might be causing your sensor not to work as expected: 1. JobSelector Usage: The
JobSelector
is used to identify a job within the sensor, but it is not directly used in the
RunRequest
. The
RunRequest
should specify the
job_name
as a string, not as a
JobSelector
object. 2. Run Configuration Format: The
run_config
you've provided appears to be a dictionary with some key-value pairs, but it's important to ensure that this dictionary is structured correctly according to the expected configuration schema of the job you're trying to run. The
run_config
should match the schema that your job expects, including any nested structures for resources, solvers, etc. 3. Tags: You are passing
context.dagster_run.tags
directly to the
RunRequest
. Ensure that these tags are appropriate for the job you're triggering and that they don't contain any information that might conflict with the job's execution. 4. Run Key: Using
context.dagster_run.run_id
as the
run_key
is generally fine, but ensure that it is unique and makes sense for your use case. If the same run ID is used for multiple runs, it might cause issues. Here's a revised version of your sensor function that addresses the first point:
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python
from dagster import run_status_sensor, DagsterRunStatus, RunRequest

@run_status_sensor(
    monitored_jobs=[elt_pipeline_weekly_job],
    run_status=DagsterRunStatus.SUCCESS,
)
def riRunDataPrepSensor(context):
    # Define the run configuration for the job you want to trigger
    run_config = {
        "solids": {
            # ... your run configuration goes here ...
        },
        "resources": {
            # ... your resources configuration goes here ...
        },
        # ... other run configuration ...
    }

    # Return a RunRequest to trigger the job
    return RunRequest(
        run_key=context.dagster_run.run_id,  # or some other unique identifier
        run_config=run_config,
        tags=context.dagster_run.tags,  # ensure these tags are appropriate
        job_name="k8s_dataprep_RI_partitioned"  # specify the job name as a string
    )
Please make sure to adjust the
run_config
to match the expected configuration schema of the job you're triggering. If you've checked these points and the sensor is still not working, consider the following debugging steps: - Check the Sensor Logs: Look at the logs for the sensor to see if there are any error messages or exceptions being raised. - Validate Run Configuration: Ensure that the
run_config
you're providing is valid for the job you're trying to ru
n. - Sensor Evaluation: Verify that the sensor is being evaluated. If it's not being evaluated, check the sensor's minimum interval and ensure that the Dagster daemon is running. - Dagster Version: Ensure that you are using a version of Dagster that supports the features you are using in your sensor. If after these checks the issue persists, you may want to consult the Dagster documentation or seek help from the Dagster community for further assistance.
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