![]() ![]() Of course, this could be improved, and you absolutely can extend or rework the code to parse manual trigger config as well (as it is shown in the video).I’m a huge fan of Apache Airflow and how the open source tool enables data engineers to scale data pipelines by more precisely orchestrating workloads.īut what happens when Airflow testing doesn’t catch all of your bad data? What if “unknown unknown” data quality issues fall through the cracks and affect your Airflow jobs? The solution extends the great ideas of this gentleman:Īlthough, this solution works with Kubernetes operators, too. The kubernetes pod task injects some variables and secrets in a simple function and uses the from .operators.kubernetes_pod import KubernetesPodOperator module, I won't and can't share those with you. The default_args, kubernetes_pod_task are just wrappers for convenience. # the callable that throws the skip signalĭef skip_task(): raise AirflowSkipException() # these are necessary to make this solution work from airflow import DAGįrom airflow.exceptions import AirflowSkipExceptionįrom import PythonOperatorįrom plugins.airflow_utils import default_args, kubernetes_pod_task This way the UI won't be confused and the trigger history will be kept complete - showing the executions when a task was skipped. Secondly, based on the config we can swap the original operator for a simple Python operator with the same name with a simple definition. Introducing the pre_execute callable with the extra DAG config takes care of the proper skip of tasks which throws the AirflowSkipException. There are no data dependencies between our tasks, we only need to follow the order of (some) tasks. The meaning of the 'catchup': True is essentially a backfill that is translated into a command line option ( -c). The strict rate limit of 1 request/s with several seconds of delay if breached would halt all parallel tasks. Occasionally the requirements change that forces us to pull the data in full but only for one/some endpoints. It's slow so we need to be selective about what we do and what we don't pull from it (1 request/s with more than 750k requests to make) We have a script that pulls data from a very crappy and slow API. Here is the idea to solve your problem: from _execute_operator import SSHExecuteOperatorįrom _rule import TriggerRuleįrom import SSHHookįirst of all, sorry for the lengthy post, but I wanted to share the complete solution that works for me. ![]() The trigger rule possibilities: ALL_SUCCESS = 'all_success' If you're installing an Airflow version >=1.10.3, you can also return a list of task ids, allowing you to skip multiple downstream paths in a single Operator and don't have to use a dummy task before joining.Īll operators have a trigger_rule argument which defines the rule by which the generated task get triggered. If you want to skip some tasks, keep in mind that you can’t have an empty path, if so make a dummy task.Ī_task = DummyOperator(task_id='branch_a', dag=dag)ī_task = DummyOperator(task_id='branch_false', dag=dag) The task_id returned by the Python function has to be referencing a task directly downstream from the BranchPythonOperator task. The task_id returned is followed, and all of the other paths are skipped. The BranchPythonOperator is much like the PythonOperator except that it expects a python_callable that returns a task_id. Airflow 1.xĪirflow has a BranchPythonOperator that can be used to express the branching dependency more directly. You can also inherit directly from BaseBranchOperator overriding the choose_branch method, but for simple branching logic the decorator is best. Return "big_task" # run just this one task, skip all else Xcom_value = int(ti.xcom_pull(task_ids="start_task")) Airflow provides a branching decorator that allows you to return the task_id (or list of task_ids) that should run: branch_func(ti): ![]()
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