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Trigger airflow dag how to#

Or, for individual tasks, you can set the maximum retry delay with the parameter, max_retry_delay. As of Airflow 2.6, you can set a maximum value for the retry delay in the core Airflow config max_task_retry_delay ( AIRFLOW_CORE_MAX_TASK_RETRY_DELAY), which, by default, is set at 24 hours. The retry_delay parameter (default: timedelta(seconds=300)) defines the time spent between retries. You can overwrite the default_task_retries of an Airflow environment at the task level by using the retries parameter. You can set this configuration either in airflow.cfg or with the environment variable AIRFLOW_CORE_DEFAULT_TASK_RETRIES. The default number of times a task will retry before failing permanently can be defined at the Airflow configuration level using the core config default_task_retries. In Airflow, you can configure individual tasks to retry automatically in case of a failure. To get the most out of this guide, you should have an understanding of:

Trigger airflow dag how to#

In this guide, you'll learn how to configure automatic retries, rerun tasks or DAGs, trigger historical DAG runs, and review the Airflow concepts of catchup and backfill.

You have a running DAG and realize you need it to process data for two months prior to the DAG's start date.

You want to deploy a DAG with a start date of one year ago and trigger all DAG runs that would have been scheduled in the past year.

You need to manually rerun a failed task for one or multiple DAG runs.

You want one or more tasks to automatically run again if they fail.

Some uses cases where you might want tasks or DAGs to run outside of their regular schedule include: The first step, parse_job_args_task is a simple PythonOperator that parses the configuration parameter customer_code provided in the DAG run configuration (a DAG run is a specific trigger of the DAG): dag = DAG(ĭag.You can set when to run Airflow DAGs using a wide variety of scheduling options. So basically we have a first step where we parse the configuration parameters, then we run the actual PDT, and if something goes wrong, we get a Slack notification. Here is what the Airflow DAG (named navigator_pdt_supplier in this example) would look like: We can do so easily by passing configuration parameters when we trigger the airflow DAG. This job will be a templated job, meaning that in order to run it we need to specify which customer database (as a parameter customer_code for example) to run it for. Lets say I have a DAG (we can call it a job) that performs some sql queries to generate a Persistent Derived Table PDT for a customer. There is a feature that Jenkins has that most schedulers do not. The improvements we gained by using an actual job scheduler are great (dag visualization, dynamic dag setup, specific task triggering among others), It is a direct competitor of other schedulers such as Spotify's Luigi or newer solutions such as DigDag or Prefect (created by core Airflow developers, I'm keeping this one on my list for future projects when it matures a bit).Īt my current company, Daltix, we are moving away from an older tool, Jenkins, a CI/CD tool we hacked so it can act as a job scheduler, to Airflow. Initially developed at Airbnb, a few years ago it became an Apache foundation project, quickly becoming one of the foundation top projects.

Airflow is one of the most widely used Schedulers currently in the tech industry.