- HOW TO MAKE A NEW FILE ON THE FLY IN PYTHON UPDATE
- HOW TO MAKE A NEW FILE ON THE FLY IN PYTHON FULL
One way of implementing a multiple-file method is using a Python script to generate DAG files based on a set of JSON configuration files. Example: Generate DAGs From JSON Config Files Changes to DAGs or additional DAGs won’t be generated until the script is run, which in some cases requires a deployment.īelow we’ll show a simple example of how this method could be implemented.On the other hand, this method includes drawbacks:
HOW TO MAKE A NEW FILE ON THE FLY IN PYTHON FULL
Since DAG files are being explicitly created before deploying to Airflow, you have full visibility into the DAG code, including from the Code button in the Airflow UI. Because the DAG files aren’t being generated by parsing code in the dags_folder, the DAG generation code isn’t executed on every scheduler heartbeat. It’s more scalable than single-file methods. 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 MethodsĪnother method for dynamically generating DAGs is to use code to generate full Python files for each DAG. We can see that all of the connections that match our filter have now been created as a unique DAG. We are also accessing the Session() class from settings, which will allow us to query the current database session. Notice that like before we are accessing the Models library to bring in the Connection class (as we did previously with the Variable class). from airflow import DAG from _operator import PythonOperator from datetime import datetime def create_dag (dag_id, schedule, dag_number, default_args): def hello_world_py ( * args): print ( 'Hello World' ) print ( 'This is DAG: schedule = dag_number = conn globals () = create_dag(dag_id, schedule, dag_number, default_args) The code here is very similar to what you would use when creating a single DAG, but it is wrapped in a method that allows for custom parameters to be passed in. In this case, we’re going to define a DAG template within a create_dag function. 
To dynamically create DAGs from a file, we 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. For more on this, see the Scalability section below.
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 parameter min_file_process_interval ( see Airflow docs).
Since this method requires a Python file in the dags_folder, the generation code will be executed every time the dag is parsed. Since a DAG file isn’t actually being created, your visibility into the code behind any specific DAG is limited. Adding DAGs is nearly instantaneous since it requires only changing the input parameters. It can accommodate input parameters from many different sources (see a few examples below). 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) (e.g. We’ll also discuss when DAG generation is a good option, and some pitfalls to watch out for when doing this at scale. In this guide, we’ll cover a few of the many ways you can generate DAGs. As long as a DAG object in globals() is created by Python code that lives in the dags_folder, Airflow will load it. In these cases, and others, it can make more sense to dynamically generate DAGs.īecause everything in Airflow is code, you can dynamically generate DAGs using Python alone. 
HOW TO MAKE A NEW FILE ON THE FLY IN PYTHON UPDATE
Or maybe you need a set of DAGs to load tables, but don’t want to manually update DAGs every time those tables change. Maybe you have hundreds or thousands of DAGs that do similar things with just a parameter changing between them. However, sometimes manually writing DAGs isn’t practical. The simplest way of creating 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. Note: All code in this guide can be found in this Github repo.
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