Store_data ( process_data ( extract_bitcoin_price ( ) ) ) > email_notification xcom_pull ( task_ids = "extract_bitcoin_price" ) Click on the Decorators tab to see the same DAG written using Airflow decorators.įrom airflow. Under the Traditional syntax tab below, there is a basic ETL DAG with tasks to get data from an API, process the data, and store it. Using decorators to define your Python functions as tasks is easy. To learn more about the TaskFlow API, check out this Astronomer webinar or this Apache Airflow TaskFlow API tutorial.
#AIRFLOW BRANCH OPERATOR HOW TO#
How to use Airflow decorators Īirflow decorators were introduced as part of the TaskFlow API, which also handles passing data between tasks using XCom and inferring task dependencies automatically.
You can also easily mix decorators and traditional operators within your DAG if your use case requires that. One exception to this is the Astro Python SDK library of decorators (more on these below), which do not have equivalent traditional operators. Generally, a decorator and the corresponding traditional operator will have the same functionality. In general, whether to use decorators is a matter of developer preference and style. Currently, decorators can be used for Python and SQL functions. The result can be cleaner DAG files that are more concise and easier to read.
#AIRFLOW BRANCH OPERATOR CODE#
The purpose of decorators in Airflow is to simplify the DAG authoring experience by eliminating the boilerplate code required by traditional operators. To get the most out of this guide, you should have an understanding of: You'll also review examples and learn when you should use decorators and how you can combine them with traditional operators in a DAG. In this guide, you'll learn about the benefits of decorators, the decorators available in Airflow, and decorators provided in the Astronomer open source Astro Python SDK library. In the context of Airflow, decorators provide a simpler, cleaner way to define your tasks and DAG. In Python, decorators are functions that take another function as an argument and extend the behavior of that function. Since Airflow 2.0, decorators have been available for some functions as an alternative DAG authoring experience to traditional operators.
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