one_success: fires as soon as at least one parent succeeds, it does not wait for all parents to be done.one_failed: fires as soon as at least one parent has failed, it does not wait for all parents to be done.all_done: all parents are done with their execution.all_failed: all parents are in a failed or upstream_failed state.all_success: (default) all parents must have succeeded.Here’s a list of all the available trigger rules and what they mean: It is also recommended to use static date times instead of dynamic dates like time.now() as dynamic dates can cause inconsistencies while deciding start date + one schedule interval because of the start date changing at every evaluation.Īirflow provides several trigger rules that can be specified in the task and based on the rule, the Scheduler decides whether to run the task or not. This is a common problem users of Airflow face trying to figure out why their DAG is not running. But this is not the case with airflow, the first instance will be run at one scheduled interval after the start date, that is at 01:00 Hrs on 1st Jan 2016. In the above example, the start date is mentioned as 1st Jan 2016, so someone would assume that the first run will be at 00:00 Hrs on the same day. There’s a small catch with the start date the DAG Run starts one schedule interval after the start_date. While creating a DAG one can provide a start date from which the DAG needs to run. This also acts as a unique identifier for each DAG Run. The execution_date is the logical date and time at which the DAG Runs, and its task instances, run. There are various things to keep in mind while scheduling a DAG. The scheduler keeps polling for tasks that are ready to run (dependencies have been met and scheduling is possible) and queues them to the executor. Airflow SchedulerĪirflow comes with a very mature and stable scheduler that is responsible for parsing DAGs at regular intervals and updating the changes if any to the database. Let’s begin with some concepts on how scheduling in Airflow works. With the help of these tools, you can easily scale your pipelines. In this blog, we will cover some of the advanced concepts and tools that will equip you to write sophisticated pipelines in Airflow. In our last blog, we covered all the basic concepts of Apache Airflow. datetime ( 2022, 1, 1 ), schedule =, tags =, ) as dag : start = EmptyOperator ( task_id = "start", ) section_1 = SubDagOperator ( task_id = "section-1", subdag = subdag ( DAG_NAME, "section-1", dag. Defaults to """ get_ip = GetRequestOperator ( task_id = "get_ip", url = "" ) ( multiple_outputs = True ) def prepare_email ( raw_json : dict ) -> dict : external_ip = raw_json return, start_date = datetime. datetime ( 2021, 1, 1, tz = "UTC" ), catchup = False, tags =, ) def example_dag_decorator ( email : str = ): """ DAG to send server IP to email. Schedule interval put in place, the logical date is going to indicate the timeĪt which it marks the start of the data interval, where the DAG run’s startĭate would then be the logical date + scheduled ( schedule = None, start_date = pendulum. However, when the DAG is being automatically scheduled, with certain Logical is because of the abstract nature of it having multiple meanings,ĭepending on the context of the DAG run itself.įor example, if a DAG run is manually triggered by the user, its logical date would be theĭate and time of which the DAG run was triggered, and the value should be equal (formally known as execution date), which describes the intended time aĭAG run is scheduled or triggered. Run’s start and end date, there is another date called logical date This period describes the time when the DAG actually ‘ran.’ Aside from the DAG Tasks specified inside a DAG are also instantiated intoĪ DAG run will have a start date when it starts, and end date when it ends. In much the same way a DAG instantiates into a DAG Run every time it’s run, Run will have one data interval covering a single day in that 3 month period,Īnd that data interval is all the tasks, operators and sensors inside the DAG Those DAG Runs will all have been started on the same actual day, but each DAG The previous 3 months of data-no problem, since Airflow can backfill the DAGĪnd run copies of it for every day in those previous 3 months, all at once. It’s been rewritten, and you want to run it on Same DAG, and each has a defined data interval, which identifies the period ofĪs an example of why this is useful, consider writing a DAG that processes aĭaily set of experimental data. If schedule is not enough to express the DAG’s schedule, see Timetables.įor more information on logical date, see Data Interval andĮvery time you run a DAG, you are creating a new instance of that DAG whichĪirflow calls a DAG Run. For more information on schedule values, see DAG Run.
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