![]() Waiting_extract_dag = ExternalTaskSensor( If we want to wait for the whole DAG we must set it to None from datetime import datetime, timedeltaįrom _task_sensor import ExternalTaskSensor Additionally, we can also specify the external_task_id identifier of a task within the DAG if we want to wait for a particular task to finish. To configure the sensor, we need the identifier of another DAG, the dag_id. Child DAGs should run on the same execution date as the parent DAG, meaning they should have the same schedule interval.Child DAGs shouldn't be manually triggered in order to get the sensor working.There are two things that the ExternalTaskSensor assumes: By default, the desired state is success. ExternalTaskSensor regularly pokes the execution state of child DAGs and waits till they get to the desired state, described in the allowed_states parameter. This external system can be another DAG when using ExternalTaskSensor. This is a great way to create a connection between the DAG and the external system. It checks whether certain criteria are met before it complete and let their downstream tasks execute. Sensors in Airflow is a special type of task. One way of signaling task completion between DAGs is to use sensors. This means that the parent DAG doesn't wait until the triggered DAGs are complete before starting the next task. But TriggerDagRunOperator works in a fire-and-forget way. Summing up, TriggerDagRunOperator can be used to run some heavy or costly dags that need to be run only when certain conditions are met. The trigger_dag_id here is simply the identification of the external DAG you want to trigger. Trigger_transform_dag.set_downstream(trigger_load_dag) Trigger_extract_dag.set_downstream(trigger_transform_dag) Trigger_load_dag = TriggerDagRunOperator( Trigger_transform_dag = TriggerDagRunOperator( Trigger_extract_dag = TriggerDagRunOperator( ![]() from datetime import datetime, timedeltaįrom _operator import TriggerDagRunOperator ,īelow is an example of a DAG that will run every 5 minutes and trigger three more DAGs using TriggerDagRunOperator. In the output we see a huge dictionary with a lot of information about the current run:, 'inlets'. To look closer at the context object, we can print it out. In the example above, a function simply returns this object, i.e. In the controller function, if the dag_run_obj object is returned, the dag will be triggered. def conditionally_trigger(context, dag_run_obj): This condition can use the execution context passed to the function and can be quite complex. So you see all dag runs in just one page instead of digging into the airflow UI which seems very convenient for me.įor TriggerDagRunOperator we need a controller, a function that controls the start of the target DAG based on some condition. The cool thing about this operator is that the DAG runs are saved in the history of these same DAGs as well as the logs. If this is not the case then they will still be triggered but will not be run - just stuck in the running state. It is necessary that the external DAGs are turned on. With this operator and external DAG identifiers, we can easily trigger them. TriggerDagRunOperator is an operator that can call external DAGs. But there are ways to achieve the same in Airflow. However, since they are not in the same DAG, we cannot do this. If those DAGs were tasks in the same DAG, we could just add those lines to the DAG file: t_downstream(transform) # Printing message at the logs and sleep for 2 secondsīash_command='echo "Extracting stuff from s3" sleep 2 ',īash_command='echo "Extracting stuff from jdbc" sleep 2 ',īash_command='echo "Transforming stuff from s3" sleep 2 ',īash_command='echo "Transforming stuff from jdbc" sleep 2 ',īash_command='echo "Loading stuff to s3" sleep 2 ',īash_command='echo "Loading stuff to hive" sleep 2 ', 'start_date': datetime.today() - timedelta(1), $ docker-compose -f docker-compose.yml up -dįrom airflow.operators import BashOperator To do this I will use this docker-compose file with Airflow, PostgreSQL pre-installed and LocalExecutor pre-configured. Let's imagine that we have an ETL process divided between 3 independent DAGs - extract, transform, and load.įor the example to be more illustrative, we need at least a Local executor so that more than one task can be run in parallel. ![]() In this post, we gonna discuss what options are available in Airflow for connecting dependent DAGs with each other. I had exactly this problem - I had to connect two independent but logically connected DAGs. It may end up with a problem of incorporating different DAGs into one pipeline. They get split between different teams within a company for future implementation and support. Often Airflow DAGs become too big and complicated to understand.
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