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# ask-ai
p

Peter Lim

02/29/2024, 4:55 PM
how do you write a sensor that tracks a job in another code location?
s

Scout

02/29/2024, 4:55 PM
To write a sensor that tracks a job in another code location, you can use the
CodeLocationSelector
and
JobSelector
identifiers to specify the code location and job you want to monitor. Here's how you can do it according to the documentation: 1. Use
CodeLocationSelector
to monitor jobs in a different code location. For example, if you want to monitor any job in a code location named "defs" when it succeeds, you can define a sensor like this:
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python from dagster import run_status_sensor, DagsterRunStatus, CodeLocationSelector  @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()
2. Use
JobSelector
if you want to monitor a specific job in another code location. For instance, to monitor a job named "data_update" in a repository named "code_location_a" within the "defs" code location, you can define a sensor like this:
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python from dagster import run_failure_sensor, JobSelector  @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()
3. If you want to monitor every job in your Dagster instance, you can set
monitor_all_code_locations=True
on the sensor decorator. However, note that
monitor_all_code_locations
cannot be used along with jobs specified via
monitored_jobs
.
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python from dagster import run_status_sensor, DagsterRunStatus  @run_status_sensor(     monitor_all_code_locations=True,     run_status=DagsterRunStatus.SUCCESS, ) def sensor_monitor_all_code_locations():     # when any job in the Dagster instance succeeds, this sensor will trigger     send_slack_alert()
Remember to replace
send_slack_alert()
with your actual alerting or notification logic. This is just a placeholder to indicate where you would put the code that should run when the sensor is triggered. These examples are based on the documentation and should guide you in setting up sensors to monitor jobs across different code locations in Dagster.
p

Peter Lim

02/29/2024, 6:51 PM
@Scout with the answer above, how do you materialize an asset selection instead of a job based on sensor evaluation?
s

Scout

02/29/2024, 6:51 PM
To materialize an asset selection instead of a job based on sensor evaluation, you can use an
asset_sensor
. An
asset_sensor
is designed to trigger a job run when there is a materialization for a specific asset or a selection of assets. Here's how you can define an
asset_sensor
that targets an `asset_selection`:
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python from dagster import asset_sensor, RunRequest, SensorEvaluationContext, AssetKey  @asset_sensor(asset_key=AssetKey("my_asset"), job=my_job) def my_asset_sensor(context: SensorEvaluationContext, asset_event):     # Logic to determine whether to trigger a job based on the asset_event     # For example, you could check the asset_event timestamp, metadata, etc.     should_trigger = True  # Replace with your condition      if should_trigger:         yield RunRequest(             run_key=asset_event.event_log_entry_id,             run_config={                 # Define the run configuration here             },         )
In the example above,
my_job
is the job that will be triggered when the sensor detects a materialization event for the asset with the key
my_asset
. The
RunRequest
is yielded if the condition
should_trigger
is met, which should be replaced with your own logic to determine when to trigger the job. If you want to target an asset selection, you can use the
asset_selection
parameter on the sensor decorator to specify a selection of assets. Here's an example:
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python from dagster import sensor, AssetSelection, RunRequest  @sensor(asset_selection=AssetSelection.keys("asset1", "asset2")) def my_asset_selection_sensor(context):     # Logic to determine whether to trigger a job based on the asset selection     should_trigger = True  # Replace with your condition      if should_trigger:         yield RunRequest(             run_key="unique_run_key",  # Replace with a unique identifier for the run             run_config={                 # Define the run configuration here             },         )
In this example, the sensor is configured to monitor a selection of assets (
"asset1"
and
"asset2"
). When the sensor evaluation logic determines that a job should be triggered, it yields a
RunRequest
with a unique
run_key
and the necessary
run_config
. Please note that the
asset_selection
parameter is used to specify which assets the sensor should monitor, and the
RunRequest
is used to trigger a job run. The job triggered by the
RunRequest
should be configured to materialize the desired assets based on the sensor's evaluation.
p

Peter Lim

02/29/2024, 7:10 PM
@Scout how do you let a run_status_sensor materialize a selection of assets instead of a job?
s

Scout

02/29/2024, 7:10 PM
As of my knowledge cutoff date in early 2023, Dagster does not support directly materializing a selection of assets from a
run_status_sensor
. The
run_status_sensor
is designed to react to the status of a job run and can be used to trigger another job or perform actions like sending alerts. It does not have built-in functionality to materialize assets directly. However, there was a discussion in a GitHub issue about enabling a
multi_asset_sensor
to submit runs for an asset selection instead of a job, which suggests that there might be interest in such a feature. This feature would allow a sensor to trigger the materialization of a specific set of assets. If you want to materialize a specific set of assets in response to a job run status, you would typically create a new job that materializes those assets and then use the
run_status_sensor
to trigger that job. Here's a conceptual example of how you might set that up:
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python from dagster import run_status_sensor, DagsterRunStatus, RunRequest  # Assume you have a job that materializes a specific set of assets asset_materialization_job = ...  @run_status_sensor(     run_status=DagsterRunStatus.SUCCESS,     request_job=asset_materialization_job, ) def asset_materialization_sensor(context):     # Check if the triggering job is not the asset materialization job itself     # to prevent an infinite loop of job triggers     if context.dagster_run.job_name != asset_materialization_job.name:         # Define the run configuration for the asset materialization job         run_config = {             # Configuration that specifies which assets to materialize         }         return RunRequest(run_key=None, run_config=run_config)     else:         # Skip triggering if it's the asset materialization job         return None
In this example, when a job run succeeds, the sensor checks if the run is not from the
asset_materialization_job
itself. If it's not, the sensor triggers the
asset_materialization_job
with the specified run configuration that materializes the desired assets. Please note that the ability to trigger materialization of a selection of assets directly from a
run_status_sensor
without involving a job would require a feature that is not currently available in Dagster as of my last update. For the most current capabilities and potential new features, please refer to the latest Dagster documentation or the Dagster GitHub repository.
p

Peter Lim

02/29/2024, 7:20 PM
@Scout how do I monitor job runs using the sensor decorator?
s

Scout

02/29/2024, 7:20 PM
To monitor job runs using the sensor decorator in Dagster, you can define a sensor that periodically checks for certain conditions and triggers job runs based on those conditions. Here's a step-by-step guide on how to create a sensor to monitor job runs: 1. Import Required Classes: First, import the necessary classes from the
dagster
module.
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python
from dagster import sensor, RunRequest
2. Define Your Sensor: Use the
@sensor
decorator to define your sensor function. The function can optionally take a
context
parameter, which is an instance of
SensorEvaluationContext
. This context provides methods and properties to access information about the Dagster instance and the sensor's evaluation.
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python
@sensor(job=my_job)
def my_sensor(context):
    # Your sensor logic here
3. Implement Sensor Logic: Inside the sensor function, implement the logic that determines when to trigger a job run. This could be based on time, external events, changes in a database, new files in a directory, or any other condition relevant to your use case. 4. Yield RunRequest: If the conditions are met, yield a
RunRequest
object to trigger a job run. The
RunRequest
can include a
run_key
to uniquely identify the run and
run_config
to provide configuration for the job.
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python
    if should_trigger_run:  # Replace with your condition
        yield RunRequest(
            run_key="unique_run_key",  # Replace with a unique identifier for the run
            run_config={
                # Define the run configuration here
            },
        )
Here's a complete example of a sensor that triggers a job run when a certain file exists:
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python
from dagster import sensor, RunRequest
import os

@sensor(job=my_job)
def file_sensor(context):
    filepath = "/path/to/my/file.txt"
    if os.path.exists(filepath):
        yield RunRequest(
            run_key="file_exists_run",  # Unique identifier for the run
            run_config={
                # Define the run configuration here
                "ops": {
                    "my_op": {
                        "config": {
                            "filename": filepath
                        }
                    }
                }
            },
        )
In this example,
my_job
is the job that will be triggered by the sensor when the file at
/path/to/my/file.txt
exists. The
run_config
is a dictionary that specifies the configuration for the job, which in this case includes the filename to be processed by an operation within the job. Remember to replace
/path/to/my/file.txt
with the actual path to the file you want to monitor, and
my_job
with the actual job you want to trigger. Please note that the sensor will run at a default interval of 30 seconds unless yo
u specify a different interval using the
minimum_interval_seconds
parameter on the
@sensor
decorator. You can adjust this interval based on your monitoring needs.
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