import SnowflakeOperatorAsync as SnowflakeOperator) is simply to make updating existing DAGs easier. Note that importing the asynchronous operator using the alias of the analogous traditional operator (e.g. To use TimeSensorAsync, remove your existing import and replace it with the following: For example, Airflow's TimeSensorAsync is a replacement of the non-deferrable TimeSensor ( source). To use a deferrable version of a core Airflow operator in your DAG, you only need to replace the import statement for the existing operator. This config can also be set with the AIRFLOW_TRIGGERER_DEFAULT_CAPACITY environment variable. You can set the number of concurrent triggers that can run in a single triggerer process with the default_capacity configuration setting in Airflow. Your output should look similar to the following image:Īs tasks are raised into a deferred state, triggers are registered in the triggerer. If you are not using Astro, run airflow triggerer to start a triggerer process in your Airflow environment. See Configure a Deployment on Astronomer Software - Triggerer to configure the triggerer. If you are using Astronomer Software 0.26 and later, you can add a triggerer to an Airflow 2.2 and later deployment in the Deployment Settings tab. If you are running Airflow on Astro or using the Astro CLI, the triggerer runs automatically if you are on Astro Runtime 4.0 and later. To use deferrable operators, you must have a triggerer running in your Airflow environment. Use deferrable operators instead of Smart Sensors, which were removed in Airflow 2.4.0. For example, using deferrable operators for sensor tasks can provide efficiency gains and reduce operational costs. The following image illustrates this process:ĭeferrable operators should be used whenever you have tasks that occupy a worker slot while polling for a condition in an external system. When the terminal status for the job is received, the task resumes, taking a worker slot while it finishes. The triggerer can run many asynchronous polling tasks concurrently, and this prevents polling tasks from occupying your worker resources. When the task is deferred, the polling process is offloaded as a trigger to the triggerer, and the worker slot becomes available. With deferrable operators, worker slots are released when a task is polling for job status. The following image illustrates this process: As worker slots are occupied, tasks are queued and start times are delayed. Although the task isn't doing significant work, it still occupies a worker slot during the polling process. With traditional operators, a task submits a job to an external system such as a Spark cluster and then polls the job status until it is completed. The terms deferrable, async, and asynchronous are used interchangeably and have the same meaning. Deferred: An Airflow task state indicating that a task has paused its execution, released the worker slot, and submitted a trigger to be picked up by the triggerer process.Running a triggerer is essential for using deferrable operators. Triggerer: An Airflow service similar to a scheduler or a worker that runs an asyncio event loop in your Airflow environment. Due to their asynchronous nature, they coexist efficiently in a single process known as the triggerer.
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