how to use the

multi_asset_sensor

to monitor missing asset in the database?

To use a

multi_asset_sensor

to monitor for missing assets in a database, you would define a sensor that checks for the expected materializations of your assets and triggers a run request if any are found to be missing. Here's a step-by-step guide based on the documentation: 1. Define the Assets: First, ensure that you have defined the assets that you want to monitor. These assets should represent tables or queries in your database. 2. Create a Job: Define a job that includes the logic for materializing the missing assets. This job will be triggered by the sensor when it detects missing materializations. 3. Define the Sensor: Use the

@multi_asset_sensor

decorator to define your sensor. You will need to specify the assets to monitor and the job to run when the sensor is triggered. 4. Implement the Sensor Logic: Inside the sensor function, use the

MultiAssetSensorEvaluationContext

to check for the latest materialization records of the monitored assets. If an asset is expected to have a new materialization but does not, this indicates that the asset is missing in the database. 5. Trigger a Run Request: If the sensor detects that an asset is missing, it should return a

RunRequest

to trigger the job that will materialize the missing asset. 6. Advance Cursors: If you are using cursors to keep track of which materializations have been processed, make sure to call

context.advance_all_cursors()

after processing the events to update the sensor's state. Here is a simplified example based on the documentation:

python from dagster import multi_asset_sensor, RunRequest, AssetKey, define_asset_job  # Define your assets and job here  @multi_asset_sensor(     monitored_assets=[AssetKey("asset1"), AssetKey("asset2")],     job=my_job,  # Replace with your actual job ) def my_missing_asset_sensor(context):     asset_events = context.latest_materialization_records_by_key()     missing_assets = [key for key, event in asset_events.items() if event is None]      if missing_assets:         # Logic to handle missing assets, e.g., trigger a job to materialize them         return RunRequest(run_key="materialize_missing_assets")      # Optionally, return a SkipReason if there are no missing assets

In this example,

latest_materialization_records_by_key

is used to fetch the most recent materialization records for each monitored asset. If any of the assets do not have a materialization record (i.e.,

event is None

), they are considered missing, and a

RunRequest

is returned to trigger a job to materialize those assets. Please note that this is a general guide, and the actual implementation may vary based on the specifics of your database and assets. You may need to customize the sensor logic to fit your particular use case.

@Scout what if the missing event is from external and not by materialization status?

If you are monitoring external assets that are not materialized by Dagster but by an external system, and you want to detect missing events, you can still use a sensor to achieve this. However, instead of checking for materialization records, you would check for the presence or absence of the expected data in your external system. Here's how you could approach this: 1. Define External Assets: Define external assets in Dagster to represent the data you expect from the external system. You can use

AssetSpec

to declare these assets. 2. Create a Job: Define a job that can handle the missing data scenario. This job might involve fetching the missing data from the external system or performing some other recovery action. 3. Define the Sensor: Use the

@sensor

decorator to define your sensor. This sensor will be responsible for checking the external system for the presence of the expected data. 4. Implement the Sensor Logic: Inside the sensor function, implement the logic to check the external system for the expected data. This could involve querying a database, checking a file system, or calling an API. 5. Trigger a Run Request: If the sensor detects that the expected data is missing, it should return a

RunRequest

to trigger the job that handles the missing data scenario. Here is a conceptual example:

python from dagster import sensor, RunRequest, AssetKey, define_asset_job  # Define your external assets and job here  @sensor(job=my_job)  # Replace with your actual job def my_external_asset_sensor(context):     # Logic to check the external system for the expected data     data_is_missing = check_external_system_for_data()      if data_is_missing:         # Logic to handle missing data, e.g., trigger a job to fetch or generate it         return RunRequest(run_key="handle_missing_data")      # Optionally, return a SkipReason if there is no missing data

In this example,

check_external_system_for_data

is a placeholder for the actual logic you would implement to check the external system for the expected data. If the data is missing, a

RunRequest

is returned to trigger a job to handle the situation. Please note that the specifics of how you check for missing data will depend on the nature of the external system and the type of data you are monitoring. You will need to tailor the sensor logic to your particular use case and the capabilities of the external system.