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Cross-DAG dependencies

When designing Airflow DAGs, it is often best practice to put all related tasks in the same DAG. However, it's sometimes necessary to create dependencies between your DAGs. In this scenario, one node of a DAG is its own complete DAG, rather than just a single task. Throughout this guide, the following terms are used to describe DAG dependencies:

  • Upstream DAG: A DAG that must reach a specified state before a downstream DAG can run
  • Downstream DAG: A DAG that cannot run until an upstream DAG reaches a specified state

The Airflow topic Cross-DAG Dependencies, indicates cross-DAG dependencies can be helpful in the following situations:

  • A DAG should only run after one or more datasets have been updated by tasks in other DAGs.
  • Two DAGs are dependent, but they have different schedules.
  • Two DAGs are dependent, but they are owned by different teams.
  • A task depends on another task but for a different execution date.

In this guide, you'll review the methods for implementing cross-DAG dependencies, including how to implement dependencies if your dependent DAGs are located in different Airflow deployments.

Assumed knowledge​

To get the most out of this guide, you should have an understanding of:

Implement cross-DAG dependencies​

There are multiple ways to implement cross-DAG dependencies in Airflow, including:

In this section, you'll learn how and when you should use each method and how to view dependencies in the Airflow UI.

Using SubDAGs to handle DAG dependencies can cause performance issues. Instead, use one of the methods described in this guide.

Dataset dependencies​

The most common way to define cross-DAG dependencies is by using datasets. DAGs that access the same data can have explicit, visible relationships, and DAGs can be scheduled based on updates to this data.

You should use this method if you have a downstream DAG that should only run after a dataset has been updated by an upstream DAG, especially if those updates are irregular. This type of dependency also provides you with increased observability into the dependencies between your DAGs and datasets in the Airflow UI.

Using datasets requires knowledge of the following scheduling concepts:

  • Producing task: A task that updates a specific dataset, defined by its outlets parameter.
  • Consuming DAG: A DAG that runs as soon as a specific dataset is updated.

Any task can be made into a producing task by providing one or more datasets to the outlets parameter. For example:

dataset1 = Dataset('s3://folder1/dataset_1.txt')

# producing task in the upstream DAG
EmptyOperator(
    task_id="producing_task",
    outlets=[dataset1]  # flagging to Airflow that dataset1 was updated
)

The following downstream DAG is scheduled to run after dataset1 has been updated by providing it to the schedule parameter.

dataset1 = Dataset('s3://folder1/dataset_1.txt')

# consuming DAG
with DAG(
    dag_id='consuming_dag_1',
    catchup=False,
    start_date=datetime.datetime(2022, 1, 1),
    schedule=[dataset1]
) as dag:

In the Airflow UI, the Next Run column for the downstream DAG shows dataset dependencies for the DAG and how many dependencies have been updated since the last DAG run. The following image shows that the DAG dataset_dependent_example_dag runs only after two different datasets have been updated. One of those datasets has already been updated by an upstream DAG.

DAG Dependencies View

See Datasets and Data-Aware Scheduling in Airflow to learn more.

TriggerDagRunOperator​

The TriggerDagRunOperator is a straightforward method of implementing cross-DAG dependencies from an upstream DAG. This operator allows you to have a task in one DAG that triggers another DAG in the same Airflow environment. For more information about this operator, see TriggerDagRunOperator.

You can trigger a downstream DAG with the TriggerDagRunOperator from any point in the upstream DAG. If you set the operator's wait_for_completion parameter to True, the upstream DAG pauses and then resumes only after the downstream DAG has finished running. This waiting process can be deferred to the triggerer by setting the parameter, deferrable, to True. This setting turns the operator into a deferrable operator, which increases Airflow's scalability and can reduce cost.

A common use case for this implementation is when an upstream DAG fetches new testing data for a machine learning pipeline, runs and tests a model, and publishes the model's prediction. In case of the model underperforming, the TriggerDagRunOperator is used to start a separate DAG that retrains the model while the upstream DAG waits. Once the model is retrained and tested by the downstream DAG, the upstream DAG resumes and publishes the new model's results.

The schedule of the downstream DAG is independent of the runs triggered by the TriggerDagRunOperator. To run a DAG solely with the TriggerDagRunOperator, set the DAG's schedule parameter to None. Note that the dependent DAG must be unpaused to get triggered.

The following example DAG implements the TriggerDagRunOperator to trigger a DAG with the dag_id dependent_dag between two other tasks. Since both the wait_for_completion and the deferrable parameters of the trigger_dependent_dag task in the trigger_dagrun_dag are set to True, the task is deferred until the dependent_dag has finished its run. Once the trigger_dagrun_dag task completes, the end_task will run.

from airflow.decorators import dag, task
from airflow.operators.trigger_dagrun import TriggerDagRunOperator
from pendulum import datetime, duration


@task
def start_task(task_type):
    return f"The {task_type} task has completed."


@task
def end_task(task_type):
    return f"The {task_type} task has completed."


# Default settings applied to all tasks
default_args = {
    "owner": "airflow",
    "depends_on_past": False,
    "email_on_failure": False,
    "email_on_retry": False,
    "retries": 1,
    "retry_delay": duration(minutes=5),
}


@dag(
    start_date=datetime(2023, 1, 1),
    max_active_runs=1,
    schedule="@daily",
    default_args=default_args,
    catchup=False,
)
def trigger_dagrun_dag():
    trigger_dependent_dag = TriggerDagRunOperator(
        task_id="trigger_dependent_dag",
        trigger_dag_id="dependent_dag",
        wait_for_completion=True,
        deferrable=True,  # Note that this parameter only exists in Airflow 2.6+
    )

    start_task("starting") >> trigger_dependent_dag >> end_task("ending")


trigger_dagrun_dag()

In the following image, you can see that the trigger_dependent_dag task in the middle is the TriggerDagRunOperator, which runs the dependent-dag.

Trigger DAG Graph

If your dependent DAG requires a config input or a specific execution date, you can specify them in the operator using the conf and execution_date params respectively.

tip

Starting with Airflow version 2.10, you can set skip_when_already_exists to True to keep the operator from attempting to trigger runs that have already occurred, and failing as a result. This can happen when trying to rerun DAGs and tasks.

ExternalTaskSensor​

To create cross-DAG dependencies from a downstream DAG, consider using one or more ExternalTaskSensors. The downstream DAG will pause until a task is completed in the upstream DAG before resuming.

This method of creating cross-DAG dependencies is especially useful when you have a downstream DAG with different branches that depend on different tasks in one or more upstream DAGs. Instead of defining an entire DAG as being downstream of another DAG as you do with datasets, you can set a specific task in a downstream DAG to wait for a task to finish in an upstream DAG.

For example, you could have upstream tasks modifying different tables in a data warehouse and one downstream DAG running one branch of data quality checks for each of those tables. You can use one ExternalTaskSensor at the start of each branch to make sure that the checks running on each table only start after the update to the specific table is finished.

A deferrable version of the ExternalTaskSensor is available, the ExternalTaskSensorAsync. For more info on deferrable operators and their benefits, see Deferrable Operators

The following example DAG uses three ExternalTaskSensors at the start of three parallel branches in the same DAG.

from airflow.decorators import dag, task
from airflow.sensors.external_task import ExternalTaskSensor
from airflow.operators.empty import EmptyOperator
from pendulum import datetime, duration


@task
def downstream_function_branch_1():
    print("Upstream DAG 1 has completed. Starting tasks of branch 1.")


@task
def downstream_function_branch_2():
    print("Upstream DAG 2 has completed. Starting tasks of branch 2.")


@task
def downstream_function_branch_3():
    print("Upstream DAG 3 has completed. Starting tasks of branch 3.")


default_args = {
    "owner": "airflow",
    "depends_on_past": False,
    "email_on_failure": False,
    "email_on_retry": False,
    "retries": 1,
    "retry_delay": duration(minutes=5),
}


@dag(
    start_date=datetime(2022, 8, 1),
    max_active_runs=3,
    schedule="*/1 * * * *",
    catchup=False,
)
def external_task_sensor_taskflow_dag():
    start = EmptyOperator(task_id="start")
    end = EmptyOperator(task_id="end")

    ets_branch_1 = ExternalTaskSensor(
        task_id="ets_branch_1",
        external_dag_id="upstream_dag_1",
        external_task_id="my_task",
        allowed_states=["success"],
        failed_states=["failed", "skipped"],
    )

    task_branch_1 = downstream_function_branch_1()

    ets_branch_2 = ExternalTaskSensor(
        task_id="ets_branch_2",
        external_dag_id="upstream_dag_2",
        external_task_id="my_task",
        allowed_states=["success"],
        failed_states=["failed", "skipped"],
    )

    task_branch_2 = downstream_function_branch_2()

    ets_branch_3 = ExternalTaskSensor(
        task_id="ets_branch_3",
        external_dag_id="upstream_dag_3",
        external_task_id="my_task",
        allowed_states=["success"],
        failed_states=["failed", "skipped"],
    )

    task_branch_3 = downstream_function_branch_3()

    start >> [ets_branch_1, ets_branch_2, ets_branch_3]

    ets_branch_1 >> task_branch_1
    ets_branch_2 >> task_branch_2
    ets_branch_3 >> task_branch_3

    [task_branch_1, task_branch_2, task_branch_3] >> end


external_task_sensor_taskflow_dag()

In this DAG:

  • ets_branch_1 waits for the my_task task of upstream_dag_1 to complete before moving on to execute task_branch_1.
  • ets_branch_2 waits for the my_task task of upstream_dag_2 to complete before moving on to execute task_branch_2.
  • ets_branch_3 waits for the my_task task of upstream_dag_3 to complete before moving on to execute task_branch_3.

These processes happen in parallel and are independent of each other. The graph view shows the state of the DAG after my_task in upstream_dag_1 has finished which caused ets_branch_1 and task_branch_1 to run. ets_branch_2 and ets_branch_3 are still waiting for their upstream tasks to finish.

ExternalTaskSensor 3 Branches

If you want the downstream DAG to wait for the entire upstream DAG to finish instead of a specific task, you can set the external_task_id to None. In the example above, you specified that the external task must have a state of success for the downstream task to succeed, as defined by the allowed_states and failed_states.

In the previous example, the upstream DAG (example_dag) and downstream DAG (external-task-sensor-dag) must have the same start date and schedule interval. This is because the ExternalTaskSensor will look for completion of the specified task or DAG at the same logical_date (previously called execution_date). To look for completion of the external task at a different date, you can make use of either of the execution_delta or execution_date_fn parameters (these are described in more detail in the documentation linked above).

Airflow API​

The Airflow API is another way of creating cross-DAG dependencies. To use the API to trigger a DAG run, you can make a POST request to the DAGRuns endpoint as described in the Airflow API documentation.

This method is useful if your dependent DAGs live in different Airflow environments (more on this in the Cross-Deployment Dependencies section below). The task triggering the downstream DAG will complete once the API call is complete.

Using the API to trigger a downstream DAG can be implemented within a DAG by using the SimpleHttpOperator as shown in the example DAG below:

from airflow.decorators import dag, task
from airflow.providers.http.operators.http import SimpleHttpOperator
from pendulum import datetime, duration
import json

# Define body of POST request for the API call to trigger another DAG
date = "{{ execution_date }}"
request_body = {"execution_date": date}
json_body = json.dumps(request_body)


@task
def print_task_type(task_type):
    """
    Example function to call before and after downstream DAG.
    """
    print(f"The {task_type} task has completed.")
    print(request_body)


default_args = {
    "owner": "airflow",
    "depends_on_past": False,
    "email_on_failure": False,
    "email_on_retry": False,
    "retries": 1,
    "retry_delay": duration(minutes=5),
}


@dag(
    start_date=datetime(2021, 1, 1),
    max_active_runs=1,
    schedule="@daily",
    catchup=False,
)
def api_dag_taskflow():
    start_task = print_task_type("starting")

    api_trigger_dependent_dag = SimpleHttpOperator(
        task_id="api_trigger_dependent_dag",
        http_conn_id="airflow-api",
        endpoint="/api/v1/dags/dependent-dag/dagRuns",
        method="POST",
        headers={"Content-Type": "application/json"},
        data=json_body,
    )

    end_task = print_task_type("ending")

    start_task >> api_trigger_dependent_dag >> end_task


api_dag_taskflow()

This DAG has a similar structure to the TriggerDagRunOperator DAG, but instead uses the SimpleHttpOperator to trigger the dependent-dag using the Airflow API. The graph view appears similar to the following image:

API Graph View

To use the SimpleHttpOperator to trigger another DAG, you need to define the following:

  • endpoint: This should be of the form '/api/v1/dags/<dag-id>/dagRuns' where <dag-id> is the ID of the DAG you want to trigger.
  • data: To trigger a DAG run using this endpoint, you must provide an execution date. In the example above, we use the execution_date of the upstream DAG, but this can be any date of your choosing. You can also specify other information about the DAG run as described in the API documentation linked above.
  • http_conn_id: This should be an Airflow connection of type HTTP, with your Airflow domain as the Host. Any authentication should be provided either as a Login/Password (if using Basic auth) or as a JSON-formatted Extra. In the example below, we use an authorization token.

Http Connection

DAG dependencies view​

The cross-DAG dependencies view shows all DAG dependencies in your Airflow environment as long as they are implemented using one of the following methods:

  • Using dataset driven scheduling
  • Using a TriggerDagRunOperator
  • Using an ExternalTaskSensor

To view dependencies in the UI, go to Browse > DAG Dependencies or by click Graph within the Datasets tab. The following image shows the dependencies created by the TriggerDagRunOperator and ExternalTaskSensor example DAGs.

DAG Dependencies View

When DAGs are scheduled depending on datasets, both the DAG containing the producing task and the dataset are shown upstream of the consuming DAG.

DAG Dependencies View Datasets

To see all dependencies between datasets and DAGs, click on the Datasets tab in the Airflow UI.

DAG Dependencies View Datasets

Cross-deployment dependencies​

It is sometimes necessary to implement cross-DAG dependencies where the DAGs do not exist in the same Airflow deployment. The TriggerDagRunOperator, ExternalTaskSensor, and dataset methods are designed to work with DAGs in the same Airflow environment, so they are not ideal for cross-Airflow deployments. The Airflow API is ideal for this use case. In this section, you'll learn how to implement this method on Astro, but the general concepts are also applicable to your Airflow environments.

Cross-deployment dependencies on Astro​

To implement cross-DAG dependencies on two different Airflow environments on Astro, follow the guidance in Cross-deployment dependencies.

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