Retry_delay = duration ( seconds = 20 ) ,īash_command = "echo I have to get it right the first time! & False" , Max_retry_delay = duration ( seconds = 10 ) ,īash_command = "echo I wait exactly 20 seconds between each of my 4 retries! & False" , T1 = BashOperator ( task_id = "t1", bash_command = "echo I get 3 retries! & False" )īash_command = "echo I get 6 retries and never wait long! & False" , "max_retry_delay" : duration ( hours = 2 ) , "retry_delay" : duration ( seconds = 2 ) , Each of the tasks uses a different retry parameter configuration.įrom airflow. The DAG below contains 4 tasks that will always fail. To override specific tasks, provide a different value to the task level retries parameter. It is common practice to set the number of retries for all tasks in a DAG by using default_args and override it for specific tasks as needed. To progressively increase the wait time between retries until max_retry_delay is reached, set retry_exponential_backoff to True. 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: 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: You can set when to run Airflow DAGs using a wide variety of scheduling options.
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