#Trigger airflow dag from python how to#
#Trigger airflow dag from python code#

One of those datasets has already been updated by an upstream DAG. The following image shows that the DAG dataset_dependent_example_dag runs only after two different datasets have been updated. 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.

Consuming DAG: A DAG that runs as soon as a specific dataset is updated.Īny task can be made into a producing task by providing one or more datasets to the outlets parameter.

Producing task: A task that updates a specific dataset, defined by its outlets parameter.

Using datasets requires knowledge of the following scheduling concepts: This type of dependency also provides you with increased observability into the dependencies between your DAGs and datasets in the Airflow UI. 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. DAGs that access the same data can have explicit, visible relationships, and DAGs can be scheduled based on updates to this data. In Airflow 2.4 and later, you can use datasets to create data-driven dependencies between DAGs. Instead, use one of the methods described in this guide. Using SubDAGs to handle DAG dependencies can cause performance issues.

#Trigger airflow dag from python how to#

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

There are multiple ways to implement cross-DAG dependencies in Airflow, including: See Managing Dependencies in Apache Airflow. To get the most out of this guide, you should have an understanding of:

#Trigger airflow dag from python code#

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.Īll code used in this is available in the cross-dag-dependencies-tutorial registry.

A task depends on another task but for a different execution date.

Two DAGs are dependent, but they are owned by different teams.

Two DAGs are dependent, but they have different schedules.

A DAG should only run after one or more datasets have been updated by tasks in other DAGs.

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

Downstream DAG: A DAG that cannot run until an upstream DAG reaches a specified state.

Upstream DAG: A DAG that must reach a specified state before a downstream DAG can run.

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