![]() ![]() 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. You can create DAG factories for all repetitive tasks you may have, thanks to this you'll be able to unit test your ETL code.For more information on schedule values, see DAG Run. Globals() = create_dag(config)Ĭreating dynamic in Airflow is super easy. Module = SourceFileLoader("module", filename).load_module() config = ",įilename = os.path.join(CONFIG_FOLDER, file) So you have a configuration folder called config in which you have the 3 sources configuration. Airflow UI with dynamic DAGs Dynamic DAGs with configurations The main reason is because Python configuration can be linted, can be statically checked and you can comment Python dicts. I recommend you to create Python configuration rather than JSON. If you want to go further you can also create a configuration per source. It creates a global variable that contains the DAG object that the Airflow DagBag will parse and add for every scheduler loop. The important part of this code is the last line. This is the DAG that loads all the raw dataįor source in : import pendulumįrom corators import dag, prepare(source): For each source we want to apply a prepare and a load function. These sources are user, product and order. Let's say we have 3 sources and we want to create a DAG per source to do stuff on each source. We will use last Airflow version - 2.3.4 - here, but it'll work for every version with the TaskFlow API.
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