![]() ![]() You could also have another DAG that runs the generation script periodically. The DAGs are generated during the CI/CD build and then deployed to Airflow. One way of implementing this method in production is to have a Python script that generates DAG files when executed as part of a CI/CD workflow. The end result of this method is having one Python file per generated DAG in your dags_folder. Multiple-file methods Īnother method for dynamically generating DAGs is to use code to generate full Python files for each DAG. You are also accessing the Session() class from settings, which will allow us to query the current database session.Īll of the connections that match our filter have now been created as a unique DAG. You are accessing the Models library to bring in the Connection class (as you did previously with the Variable class). Globals ( ) = create_dag ( dag_id, schedule, dag_number, default_args ) decorators import dag, taskĭef create_dag ( dag_id, schedule, dag_number, default_args ) ( dag_id = dag_id, schedule = schedule, default_args = default_args, catchup = False ) The code here is very similar to what you would use when creating a single DAG, but it is wrapped in a function that allows for custom parameters to be passed in.įrom airflow. In this case, you're going to define a DAG template within a create_dag function. To dynamically create DAGs from a file, you need to define a Python function that will generate the DAGs based on an input parameter. In the following examples, the single-file method is implemented differently based on which input parameters are used for generating DAGs. This can cause performance issues if the total number of DAGs is large, or if the code is connecting to an external system such as a database. ![]() How frequently this occurs is controlled by the min_file_process_interval parameter. Generation code is executed every time the DAG is parsed because this method requires a Python file in the dags_folder.Your visibility into the code behind any specific is limited because a DAG file isn't created.The single-file method has the following disadvantages: Adding DAGs is nearly instantaneous since it requires only changing the input parameters.It can accommodate input parameters from many different sources. ![]() This requires creating many DAGs that all follow a similar pattern. A common use case for this is an ETL or ELT-type pipeline where there are many data sources or destinations. One method for dynamically generating DAGs is to have a single Python file which generates DAGs based on some input parameter(s). To get the most out of this guide, you should have an understanding of: Due to its higher degree of support and stability, we recommend exploring dynamic task mapping for your use case before implementing the dynamic DAG generation methods described in this guide. Dynamic task mapping is a first-class Airflow feature, and is suitable for many dynamic use cases. You'll learn when DAG generation is the preferred option and what pitfalls to avoid.Īll code used in this guide is located in the Astronomer Registry.Īs of Airflow 2.3, you can use dynamic task mapping to write DAGs that dynamically generate parallel tasks at runtime. In this guide, you'll learn how to dynamically generate DAGs. As long as a DAG object in globals() is created by Python code that is stored in the dags_folder, Airflow will load it. In these cases, and others, it makes more sense to dynamically generate DAGs.īecause everything in Airflow is code, you can dynamically generate DAGs using Python alone. Or maybe you need a set of DAGs to load tables, but don't want to manually update DAGs every time the tables change. Maybe you have hundreds or thousands of DAGs that do similar things with just a parameter changing between them. Sometimes, manually writing DAGs isn't practical. The simplest way to create a DAG is to write it as a static Python file. Airflow executes all Python code in the dags_folder and loads any DAG objects that appear in globals(). In Airflow, DAGs are defined as Python code.
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