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1. #AIRFLOW XCOM PYTHONOPERATOR HOW TO#
2. #AIRFLOW XCOM PYTHONOPERATOR INSTALL#
3. #AIRFLOW XCOM PYTHONOPERATOR CODE#

If you don’t know what I’m talking about, check this article.įor instance, let’s say you want to fetch the accuracy by pulling a XCom, you can do that: def _choose_best_model(ti):Īccuracy = ti.xcom_pull(key='accuracy', task_ids=) You can give additional arguments through op_kwargs and op_args. You can access the context of the task instance to pull XComs. As a result, parameters of PythonOperator are accessible in the BranchPythonOperator. The BranchPythonOperator inherits from the PythonOperator.

#AIRFLOW XCOM PYTHONOPERATOR HOW TO#

How to Use AsyncTask for Android Behind the scene of the BranchPythonOperator Here, the function returns “accurate”, therefore, the next task to tigger is “accurate”. You can see the condition returning the task id, either “accurate” or “inaccurate”.

If you take a look at the python function _choose_best_result(). It expects a task_id and a python_callable function. Pay attention to the arguments of the BranchPythonOperator.

#AIRFLOW XCOM PYTHONOPERATOR CODE#

Copy paste the code in that file and execute the command docker-compose up -d in the folder docker-airflow.

Create a file branching.py in the folder airflow-data/dags. Airflow 2.0, not 1.10.14 😎 Clone the repo, go into it. Then, go to my beautiful repository to get the docker compose file that will help you running Airflow on your computer.

#AIRFLOW XCOM PYTHONOPERATOR INSTALL#

To run the code, install Docker on your computer. The code above gives you the same data pipeline as shown before. With DAG('branching', default_args=default_args, catchup=False) as dag:Ĭhoose_best_model = BranchPythonOperator(Ĭhoose_best_model > What about a bit of code to implement it? from airflow import DAGįrom import BranchPythonOperatorįrom import DummyOperator In practiceĪll right, you know the BranchPythonOperator and you know how it works. In the example, if you put a task after “Is inaccurate”, that task will be skipped. Consequently, downstream tasks that are not returned by the BranchPythonOperator get skipped! Also, tasks following skipped tasks are skipped as well. In our case, “Choosing Best ML” and “Is accurate” have succeeded whereas “Is inaccurate” has been skipped. Can you guess which task is executed next? “Is accurate” or “Is inaccurate”? Like the PythonOperator, the BranchPythonOperator executes a Python function returning the task id of the next task to execute. That condition is evaluated in a python callable function. The BranchPythonOperator allows to follow a specific path according to a condition. Ok, we are to happy to meet with the BranchPythonOperator but what does it do? Give a warm welcome to the BranchPythonOperator! The BranchPythonOperator Yes there is! And you know it, otherwise you wouldn’t be there 😅 How can you do this? Is there a mechanism to achieve this? Let’s say, if the accuracy is above 5.0 we trigger “Is accurate”, otherwise, “Is inaccurate” is run. Then, either the task “is accurate” or “is inaccurate” should get executed according to the accuracy of the best ML model. Once they all complete, “Choosing Best ML” task is getting triggered. The first three tasks are training machine learning models. Msg = ti.Quick explanation about the DAG. Task_instance.xcom_push(key="the_message", value=msg) # start_date=datetime(2019, 04, push_function(**context): Used the same code and modified params like Startdate etc. Python_callable=obj.func_archive_s3_file, However in the case of fetching an XCom value, another alternative is just using the TaskInstance object made available to you via context: def func_archive_s3_file(**context):Īrchive(context.xcom_pull(task_ids='submit_file_to_spark')) Is provide_context=True necessary for both functions?Īny edits to make this answer clearer are very welcome!.What's happening with ti here? How is that built in to **kwargs?.I'm not sure why this works, but it does. Super simple: from datetime import datetimeįrom _operator import PythonOperator Referencing this question and this XCom example got me to the following solution. Upvoted both the question and the answer, but I think that this can be made a little more clear for those users who just want to pass small data objects between PythonOperator tasks in their DAGs.