The ExternalTaskSensor for Dag Dependencies I tend to use it, especially for cleaning metadata generated by DAG Runs over time. The TriggerDagRunOperator is perfect if you want to trigger another DAG between two tasks like with SubDAGs (don’t use them 😉). Usually, it implies that the targer_dag has a schedule_interval to None as you want to trigger it explicitly and not automatically. Make sure that the target_dag is unpaused otherwise the triggered DAG Run will be queued and nothing will happen.
# Data received from the TriggerDagRunOperator Start_date=datetime(2022, 1, 1), catchup=False) as start(dag_run=None): With DAG('target_dag_1_0', schedule_interval=None, In this case, you would have a variable target_dag_version with the values 1.0, 2.0, etc. The example below can be useful if you version your target DAG and don’t want to push a new DAG where the TriggerDagRunOperator is just to change the version. That means you can inject data at run time that comes from Variables, Connections, etc. Trigger_dag_id is also a templated parameter. If DAG A triggers DAG B, DAG A and DAG B must be in the same Airflow environment. Notice that the DAG “target_dag” and the DAG where the TriggerDagRunOperator is implemented must be in the same Airflow environment. Trigger_dag_id='target_dag', # the dag to trigger For example, if trigger_dag_id=”target_dag”, the DAG with the DAG id “target_dag” will be triggered. The trigger_dag_id parameter defines the DAG ID of the DAG to trigger. Let’s take a look at the parameters you can define and what they bring. It allows you to have a task in a DAG that triggers another DAG in the same Airflow instance. The TriggerDagRunOperator is the easiest way to implement DAG dependencies in Apache Airflow.
#Trigger airflow dag how to
That’s why the arrows are opposite, unlike in the previous example.Īll right, now you have the use cases in mind, let’s see how to implement them! TriggerDagRunOperator The role of the “trigger” task is to trigger another DAG when a condition is met. How? With the “trigger” tasks! Notice that each DAG on the left has the “trigger” task at the end. The DAG on the right is in charge of cleaning this metadata as soon as one DAG on the left completes. The three DAGs on the left are still doing the same stuff that produces metadata (XComs, task instances, etc). But, if you carefully look at the red arrows, there is a major change. # Warnings can be treated as errors or successes depending on your use case.The example above looks very similar to the previous one.
# This task waits for the sync status to complete or error before completing. My_async_task = HightouchTriggerSyncOperator( # This task submits the request but doesn't wait for completion of the sync before completing.
#Trigger airflow dag full
When the task is run, Airflow sends a call to the Hightouch API to trigger a run which completes asynchronously.Īlternatively, you can choose to send a synchronous request and await the results of the sync before completing the task.įor a full description of the operator parameters, view the source code in the GitHub repository.
The last step is to add the operator to a DAG and enter the sync ID of the sync you want to trigger.
#Trigger airflow dag install
pip install airflow-provider-hightouchĪdd the HightouchTriggerSyncOperator to your DAG While the details will vary for your particular Airflow installation, the simplest way to install our package is through pip.
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