#Airflow kwargs code

However, managing the connections and variables that these pipelines depend on can be a challenge, especially when dealing with multiple environments or teams. It turns out that the timeout is evaluated after the function is called, so if my function returns False, the task sleeps for 60 seconds before it re-evaluates the conditions (configurable using the poke_interval parameter, 60 seconds by default).Data scientists and engineers have made Apache Airflow a leading open source tool to create data pipelines due to its active open source community, familiar Python development as directed acyclic graph (DAG) workflows, and extensive library of prebuilt integrations. Strangely, because of that, the PythonSensor waits over a minute before it skips the tasks. To change the maximal waiting time, I change the timeout parameter to 10 seconds. I donвЂ™t want to do that, so the sensor must not wait for that long. By default, it calls the Python function every minute for a week before it gives up! That would be ok if I were querying the DynamoDB table inside the function called by the PythonSensor. The default BaseSensorOperator parameters (inherited by PythonSensor) are not ideal in this case. When I set soft_fail to True, it skips the tasks instead of marking them as failed. Fortunately, there is a simple configuration parameter that changes the sensor behavior. If the DAG has nothing to backfill, it should skip all the remaining tasks, not fail the DAG. xcom_pull ( 'retrieve_next_backfill_date', key = 'return_value' ) return backfill_date PythonSensor ( task_id = 'should_continue_backfill', python_callable = should_continue_backfill, provide_context = True, timeout = 10, dag = dag )Д«y default, the sensor either continues the DAG or marks the DAG execution as failed. Note that I need access to the task instance, so the function run by the PythonOperator takes the Airflow context as a parameter, and I have to set provide_context to True.Дef should_continue_backfill ( ** kwargs ): task_instance = kwargs backfill_date = task_instance. Let me show you an example in which I use both methods. I can either return a value from the function running in the PythonOperator, which automatically assigns it to the return_value variable of the task or explicitly call the xcom_push function inside my function. Of course, after reading the backfill parameters, it marks the particular backfill as started in the DynamoDB to avoid reprocessing in case of DAG failures.ДЄs you saw in the subsection title, I use the XCom feature to store the backfill parameters. No long-running screen sessions with SSH connection to Airflow to make sure that the backfill schedules all dates.Д«ecause of that, my backfill DAG reads the execution date from a DynamoDB table. I want to run only one script that setups the backfill and donвЂ™t do anything more. I am not using the backfill feature available in Airflow because I need to pass more parameters than just an execution date.ДЄlso, it annoys me that the backfill feature does not schedule all DAG runs at once.

#Airflow kwargs code

I will bet my money again that the author of that task is not going to read the documentation or other task source code before making the change. Second, at some point, someone is going to add another task to that DAG. It is supposed to destroy the existing data and replace it with the freshly calculated values. ItвЂ™s not ideal for two reasons.ДЇirst, the sole purpose of a backfill DAG is to have an easy way to overwrite files when we need to fix something. The problems started with a simple question: what is the DAG supposed to do when there is nothing to backfill?ДЇor sure, we can manually disable it when we donвЂ™t need the DAG, but I am willing to bet my money on that someone will forget to pause the DAG, and it is going to run when it is not supposed to do anything.ДЄnother way to avoid problems is to make sure that all tasks in the DAG donвЂ™t break anything if the output already exists. Recently, I implemented a DAG, which we are going to use for data backfills. The more I use Airflow, the stranger uses cases I find.