--- title: 'Understanding Airflow Trigger Rules: A Comprehensive Visual Guide' description: >- Discover the intricacies of Airflow trigger rules with visual examples and practical applications. Learn how to define and use various trigger rules to optimize your DAGs efficiently in Airflow. Essential reading for Airflow users working with version 2.9.2. date: 2024-07-26T00:00:00.000Z authors: - author: src/content/people/tamara-fingerlin.md tags: [] categories: [] canonical_url: >- https://www.astronomer.io/blog/understanding-airflow-trigger-rules-comprehensive-visual-guide/ --- ## Introduction to Airflow Trigger Rules The core purpose of Airflow is to orchestrate tasks and every single task in Airflow has a trigger rule assigned. By default, Airflow runs a task when all directly upstream tasks are successful using the trigger rule on\_success. This works for many simple pipelines but as soon as DAG structures become more complex you might want to run a task if only one of its upstream tasks succeeded, or even in the case of upstream task failure. This can be achieved by changing the trigger\_rule parameter of a task, which determines when a task runs in relation to the previous tasks it [depends on](https://www.astronomer.io/docs/learn/managing-dependencies). In this blog post you will learn everything you need to know about all Airflow trigger rules as of Airflow version 2.9.2. For each trigger rule one or more screenshots of a typical scenario of using it is shown to help you pick and remember the right trigger rule for every DAG-situation. ## What are Airflow trigger rules? ### How to define a trigger rule in Airflow You can explicitly set the trigger rule of a task by setting its `trigger_rule` parameter. ## List of available Airflow trigger rules The following trigger rules are available: * `all_success`: (default) The task runs only when all upstream tasks have succeeded. * `all_failed`: The task runs only when all upstream tasks are in a failed or upstream\_failed state. * `all_done`: The task runs once all upstream tasks are done with their execution. * `all_skipped`: The task runs only when all upstream tasks have been skipped. * `one_failed`: The task runs when at least one upstream task has failed. * `one_success`: The task runs when at least one upstream task has succeeded. * `one_done`: The task runs when at least one upstream task has either succeeded or failed. * `none_failed`: The task runs only when all upstream tasks have succeeded or been skipped. * `none_failed_min_one_success`: The task runs only when all upstream tasks have not failed or upstream\_failed, and at least one upstream task has succeeded. * `none_skipped`: The task runs only when no upstream task is in a skipped state. * `always`: The task runs at any time. ## Special cases related to Airflow trigger rules There are several special cases where trigger rules play an important role: 1. [Branching](/docs/learn/airflow-branch-operator): The @task.branch decorator or the BranchPythonOperator allow you to use custom logic to decide which of a set of downstream tasks should run next and which should be skipped. It is important to adjust downstream trigger rules to allowed for skipped upstream tasks. See [Branching and trigger rules](https://www.astronomer.io/docs/learn/airflow-trigger-rules). 2. fail\_stop: You can define a DAG in which any task failure stops the DAG execution by setting the [DAG parameter](https://www.astronomer.io/docs/learn/airflow-dag-parameters) `fail_stop` to `True`. This will set all tasks that are still running to `failed` and mark any tasks that have not run yet as `skipped`. In a DAG using fail\_stop you cannot have any trigger rules other than all\_success. 3. Setup and teardown tasks are a special type of task to create and delete resources that also influence trigger rules. See [Use setup and teardown tasks in Airflow](https://www.astronomer.io/docs/learn/airflow-setup-teardown). ## Visual examples of Airflow trigger rules ### Trigger rule `all_success` A task with the trigger rule `all_success` only runs when all upstream tasks have succeeded. Screenshot of the Airflow UI with a DAG graph showing 4 successful upstream tasks and one successful downstream task depending on all 4 upstream tasks As soon as any upstream tasks are in the state of `failed` or `upstream_failed`, the downstream task is set to the state `upstream_failed` and does not run. Screenshot of the Airflow UI with a DAG graph showing 2 successful, 1 running and 1 failed upstream tasks and one downstream task in upstream\_failed state depending on all 4 upstream tasks Similarly, as soon as any upstream task is in the state `skipped`, the downstream task is set to the state `skipped` and does not run. Screenshot of the Airflow UI with a DAG graph showing 2 successful, 1 running and 1 skipped upstream tasks and one downstream task in skipped state depending on all 4 upstream tasks If a task with the trigger rule `all_success` has one upstream task that is skipped and one that is failed, whether the downstream task is set to `skipped` or `upstream_failed` depends on which of the upstream tasks finishes first. ### Trigger rule `all_failed` A task with the trigger rule `all_failed` only runs when all upstream tasks are in a failed or upstream\_failed state. Screenshot of the Airflow UI with a DAG graph showing 2 failed and two upstream failed upstream tasks and one successful downstream task depending on all 4 upstream tasks As soon as any upstream task is in the state `success`, the downstream task is set to the state `skipped` and does not run. Screenshot of the Airflow UI with a DAG graph showing 1 failed, 1 successful, 1 upstream failed and 1 running upstream tasks and one downstream task in skipped state depending on all 4 upstream tasks Analogously, as soon as any upstream task is in the state `skipped`, the downstream task is set to the state `skipped` and does not run. Screenshot of the Airflow UI with a DAG graph showing 1 failed, 1 running and 1 skipped, 1 upstream failed upstream tasks and one downstream task in skipped state depending on all 4 upstream tasks ### Trigger rule `all_done` The `all_done` trigger rule will make a task wait until all upstream tasks are done with their execution. Screenshot of the Airflow UI with a DAG graph showing 1 failed, 1 running, 1 skipped and one upstream failed upstream tasks and one downstream task in running state depending on all 4 upstream tasks As soon as all tasks finish, no matter what their state is, the downstream task will run. Screenshot of the Airflow UI with a DAG graph showing 1 failed, 1 successful, 1 skipped and one upstream failed upstream tasks and one downstream task in success state depending on all 4 upstream tasks ### Trigger rule `all_skipped` A task with the trigger rule `all_skipped` only runs when all upstream tasks have been skipped. Screenshot of the Airflow UI with a DAG graph showing 4 skipped upstream tasks and one successful downstream task depending on all 4 upstream tasks As soon as any upstream task is in the state `success`, `failed`, or `upstream_failed`, the downstream task with the trigger rule `all_skipped` is set to the state `skipped` and does not run. Screenshot of the Airflow UI with a DAG graph showing 1 successful, 3 running and one queued upstream task and one downstream task in skipped state depending on all 4 upstream tasks ### Trigger rule `one_failed` The `one_failed` trigger rule will make a task run as soon as at least one of its upstream tasks is in either the `failed` or `upstream_failed` state. Screenshot of the Airflow UI with a DAG graph showing 1 failed, 3 running and 1 queued upstream tasks and one downstream task in success state depending on all 4 upstream tasks Screenshot of the Airflow UI with a DAG graph showing 1 upstream failed, 3 running and 1 queued upstream tasks and one downstream task in success state depending on all 4 upstream tasks If all upstream tasks have completed and none of them are in the `failed` or `upstream_failed` state, the downstream task will be set to the state `skipped`. Screenshot of the Airflow UI with a DAG graph showing 2 successful and 2 skipped upstream tasks and one downstream task in skipped state depending on all 4 upstream tasks ### Trigger rule `one_success` The `one_success` trigger rule will make a task run as soon as at least one of its upstream tasks is in the `success` state. Screenshot of the Airflow UI with a DAG graph showing 1 successful, 3 running and 1 queued upstream tasks and one downstream task in success state depending on all 4 upstream tasks If all upstream tasks have been skipped, the downstream task with the `one_success` trigger rule is set to the state `skipped` as well. Screenshot of the Airflow UI with a DAG graph showing 4 skipped upstream tasks and one downstream task in skipped state depending on all 4 upstream tasks If all upstream tasks have completed and at least one of them is in the `failed` or `upstream_failed` state, the downstream task will be set to the state `upstream_failed`. Screenshot of the Airflow UI with a DAG graph showing 2 failed, 1 upstream failed and 1 skipped upstream tasks and one downstream task in upstream\_failed state depending on all 4 upstream tasks ### Trigger rule `one_done` The `one_done` trigger rule makes a task run as soon as at least one of its upstream tasks is in either the `success` or `failed` state. Upstream tasks with `skipped` or `upstream_failed` states are not considered. Screenshot of the Airflow UI with a DAG graph showing 1 upstream failed, 1 skipped and 2 running upstream tasks and one downstream task in queued state depending on all 4 upstream tasks Once one upstream task finishes (either in the `success` or `failed` state), the downstream task runs. Screenshot of the Airflow UI with a DAG graph showing 1 successful, 1 upstream failed, 1 skipped and 1 running upstream tasks and one downstream task in success state depending on all 4 upstream tasks If all upstream tasks are either in `skipped` or `upstream_failed` states, the downstream task with the `one_done` trigger rule is set to the state `skipped`. Screenshot of the Airflow UI with a DAG graph showing 2 upstream failed and 2 skipped tasks and one downstream task in skipped state depending on all 4 upstream tasks ### Trigger rule `none_failed` The `none_failed` trigger rule makes a task run only when all upstream tasks have either succeeded or been skipped. Screenshot of the Airflow UI with a DAG graph showing 2 successful and 2 skipped upstream tasks and one downstream task in success state depending on all 4 upstream tasks As soon as any upstream task is in the state `failed` or `upstream_failed`, the downstream task is set to the state `upstream_failed` and does not run. Screenshot of the Airflow UI with a DAG graph showing 1 failed, 3 running upstream tasks and one downstream task in upstream\_failed state depending on all 4 upstream tasks ### Trigger rule `none_failed_min_one_success` Tasks using the `none_failed_min_one_success` trigger rule run only when three conditions are met: 1. All upstream tasks are finished. 2. No upstream tasks are in the `failed` or `upstream_failed` state. 3. At least one upstream task is in the `success` state. Screenshot of the Airflow UI with a DAG graph showing 2 successful and 2 skipped upstream tasks and one downstream task in success state depending on all 4 upstream tasks If any upstream task is in the `failed` or `upstream_failed` state, the downstream task is set to the state `upstream_failed` and does not run. Screenshot of the Airflow UI with a DAG graph showing 1 failed, 1 successful, 1 skipped and 1 upstream failed upstream task and one downstream task in upstream\_failed state depending on all 4 upstream tasks If all upstream tasks are in the `skipped` state, the downstream task is set to the state `skipped` and does not run. Screenshot of the Airflow UI with a DAG graph showing 4 skipped upstream tasks and one downstream task in skipped state depending on all 4 upstream tasks ### Trigger rule `none_skipped` Tasks using the `none_skipped` trigger rule run only when no upstream task is in the `skipped` state. Upstream tasks can be in any other state: `success`, `failed`, or `upstream_failed`. Screenshot of the Airflow UI with a DAG graph showing 2 successful, 1 failed and 1 upstream failed upstream tasks and one downstream task in success state depending on all 4 upstream tasks If any upstream task is in the `skipped` state, the downstream task is set to the state `skipped` and does not run. Screenshot of the Airflow UI with a DAG graph showing 1 successful, 1 failed, 1 skipped and 1 upstream failed upstream tasks and one downstream task in skipped state depending on all 4 upstream tasks ### Trigger rule `always` A task with the trigger rule `always` runs as soon as the DAG run is started, regardless of the state of its upstream tasks. Screenshot of the Airflow UI with a DAG graph showing 4 running upstream tasks and one downstream task in running state depending on all 4 upstream tasks ## Conclusion and Next steps Congrats! Now you know all about Airflow trigger rules and are ready to use them in your DAGs! To experience how easy it is to run these DAGs in production, sign up now for [a free Astro trial](https://www.astronomer.io/lp/signup/).