![]() XCom.execution_date = execution_date).all() XCom.dag_id = dag_id, XCom.task_id = task_id, Xcoms: List = session.query(XCom).filter( :param session: Airflow's SQLAlchemy Session (this param must not be passed, it will be automatically supplied decorator) Session: Optional) -> List:įunction that reads and returns 'values' of XCOMs with given filters Here's an untested code snippet for referenceįrom import provide_sessionįrom pendulum import read_xcom_values(dag_id: str, (without having to create a task or DAG). So you want to access XCOM outside Airflow (probably a different project / module, without creating any Airflow DAGs / tasks)?Īirflow uses SQLAlchemy for mapping all it's models (including XCOM) to corresponding SQLAlchemy backend (meta-db) tables Now I would like to pass this value to some python function sql_file_template without using PythonOperator.Īs per Airflow documentation xcom can be accessed only between tasks. The output of stored proc is a string which is captured using xcom. Task_instance.xcom_push(key='query_string', value=result) Query = query.format(kwargs,kwargs ,kwargs,kwargs,kwargs) Query = """CALL `.dataset_name.store_proc`( Stay tuned for that, and I’ll make sure to publish the article in a couple of days.I have a stored XCom value that I wanted to pass to another python function which is not called using PythonOperator. In the following article, you’ll learn the right way of communicating with REST APIs in Airflow, with HttpSensor and HttpOperator. Don’t use XComs to exchange huge datasets and you’re good to go. Just remember that Airflow isn’t a data processing framework, but a data orchestrator instead. You now have everything needed to effectively communicate between tasks in your DAGs. Today you’ve learned the basics of Airflow XComs. Process big datasets in Spark, and use Airflow only to trigger a Spark job.Īt the end of the day, Airflow is an orchestrator, and it should be used for that purpose only. You’re likely to run into memory issues if you try to exchange large datasets between the tasks. Airflow is not a data processing framework, so avoid sending huge Pandas DataFrames between tasks. XComs might seem like a be-all-end-all solution to communicate between tasks in Airflow, but there are some limitations you should know. Are there any limitations you should be aware of? Let’s cover these next. Here’s what it looks like on my end:Īnd that’s how you can communicate between Airflow tasks with XComs. Image 3 - Testing an Airflow task through Terminal (2) (image by author)Īssuming everything went well, you’ll see a new date.txt file created. Here’s the code for the task and for the function: It’s a list - Access the members of the pulled values with Python’s list indexing notation. ![]() Specify a list of task IDs from which you want to fetch values stored in XComs. ![]() ![]()
0 Comments
Leave a Reply. |