Composer jobs8/11/2023 Below a code snippet is shown that is used to trigger an external DAG.Īs a side note: code such as the 'condition_param' options works through the airflow context, which acts as a sort of python library that stores the context of the current airflow job. Defining the trigger DAG is pretty straightforward. Triggering external DAGsĪs the name suggests, the TriggerDagRunOperator is used to trigger other DAGs. One way of doing is, is using the TriggerDagRunOperator. However, when using a master controller we need to interact between different Airflow DAG files. When working with only one independent Airflow job we are only interacting with Apache Beam. From now on, the master_controller will be referred to as M. Furthermore, we will use a master controller DAG that will govern the sequence of events. For illustration purposes we will use Airflow DAGs A and B which are used to trigger individual Beam jobs. This post will outline how to make a master controller in the situation of using Airflow to trigger Beam jobs for ETL tasks. General outline: Making a master controller This is where the master controller comes in. Apart from Airflow DAGs triggering the individual Beam jobs, we need a separate DAG to govern the sequence in which these individual triggers are going to be run. One Beam job can only run after another has completely finished, and some Beam jobs can’t run at all if one fails. In this case Beam jobs are dependent on each other. For instance, if a certain table needs to look up values in another table, data needs to be available before starting the Beam job. But in some cases it may be necessary to have data from one Beam job available in the database before another Beam job starts. This task can easily be orchestrated using Airflow with the Beam operator. Suppose we want to write data to a database using Apache Beam. The challenge: The interdependence of Beam jobs However, we need a different approach when different Airflow sequences become more complex and dependent on each other’s success. This approach works well in situations where tasks are simple and independent from each other. This is convenient because all orchestration logic is in one place, while the purpose of the document stays clear because it only caters to one specific sequence of events. Generally, a single Airflow job is written in a single Python document. One can interact with BigQuery, start Apache Beam jobs and move documents around in Google Cloud Storage, just to name a few. Apache Airflow is a great way to orchestrate jobs of various kinds on Google Cloud.
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