![]() The usage of TriggerDagRunOperator is quite simple. Parameters: triggerdagid (str) the dagid to trigger pythoncallable (python callable) a reference to a python function that will be called while. Perhaps, most of the time, the TriggerDagRunOperator is just overkill. Trigger Airflow DAGs via the REST API Last updated on 5 min read Data Management This post will discuss how to use the REST api in Airflow 2 to trigger the run of a DAG as well as pass parameters that can be used in the run. ![]() Still, all of those ideas a little bit exaggerated and overstretched. For example, when the input data contains some values. The next idea I had was extracting an expansive computation that does not need to run every time to a separate DAG and trigger it only when necessary. The SDK makes use of the Argo models defined in the Argo Python client repository. On the other hand, if I had a few DAGs that require the same compensation actions in case of failures, I could extract the common code to a separate DAG and add only the BranchPythonOperator and the TriggerDagRunOperator to all of the DAGs that must fix something in a case of a failure. If DAG B depends only on an artifact that DAG A generates, such as a. I could put all of the compensation tasks in the other code branch and not bother using the trigger operator and defining a separate DAG. However, that does not make any sense either. Popular Use Case It’s a great tool for orchestrating ETL pipelines and monitor them as they run. High Level What does it do It allows you to schedule tasks to run, run them in a particular order, and monitor / manage all of your tasks. In the other branch, we can trigger another DAG using the trigger operator. What is Airflow It is a platform to programmatically author, schedule and monitor workflows. If you want to run the dag in webserver you need to place dag. ![]() We can use the BranchPythonOperator to define two code execution paths, choose the first one during regular operation, and the other path in case of an error. The python dag.py command only verify the code it is not going to run the dag. ![]() The next idea was using it to trigger a compensation action in case of a DAG failure. There is a concept of SubDAGs in Airflow, so extracting a part of the DAG to another and triggering it using the TriggerDagRunOperator does not look like a correct usage. I wondered how to use the TriggerDagRunOperator operator since I learned that it exists. This article is a part of my "100 data engineering tutorials in 100 days" challenge. ![]()
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