![]() ![]() For a full description of functionality, check out the Astro Python SDK documentation. It also allows you to transition seamlessly between SQL and Python for transformations without having to explicitly pass data between tasks or convert the results of queries to dataframes and vice versa. The library contains SQL and dataframe decorators that greatly simplify your DAG code and allow you to directly define tasks without boilerplate operator code. The goal is to allow DAG writers to focus on defining execution logic without having to worry about orchestration logic. The Astro Python SDK provides decorators and modules that allow data engineers to think in terms of data transformations rather than Airflow concepts when writing DAGs. Note that when adding traditional operators, dependencies are still defined using bitshift operators. 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. 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. 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. ![]() Species = df.groupby('species').agg( INFO - Marking task as SUCCESS.Since Airflow 2.0, decorators have been available for some functions as an alternative DAG authoring experience to traditional operators. It's obvious that we can access data from another DAG.įrom _operator import PythonOperator How? Variables store data in Metadata database. Simply put, Xcoms are used among inter-task communication and Variables are used not only for inter-task communication but also for inter-DAG communication. Oh, I forgot to mention that I will focus on XCom and skip Variable for the reason mentioned in the official document. since it can range from a simple string to something like Pandas DataFrame. ![]() How should we deal with this case? I would first have a look at what objects we're trying to pass. In other words, the output of a certain task can be used as an input of another task. They are different methods but do need to share data. When defining the _init_ method, we sometimes define parameters that are going to be accessed from other methods. But what if we have to exchange data between tasks? Think it as we're defining a Class with some methods. In general, tasks in a DAG are recommended to be independent from one another. ![]()
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