Our DAG would have run a few times by now. In our case, if a row corresponding to a given id exists in sample.output_data it will be updated, else a new record will be inserted into the sample.output_data table. This is a postgres feature that allows us to write UPSERT (update or insert) queries based on a unique identifier(id in our case).
ON CONFLICT (id) DO UPDATE: We use this to keep records in our output unique.SELECT ': Since execution_date is a datetime Pendulum object, we can use any of pendulum’s functions.hour is one of those functions which provides the hour as a number between 0 and 23. Let’s create a file called sample_dag.py in the current directory within the dags folder.įrom _operator import PostgresOperator Let’s assume we have an Airflow DAG set to run every hour, starting at 00 UTC, which takes some input and generates an output. Now that we know what the execution_date is, we can use that to backfill already processed data.
If you have uneven or complex schedules, note that Airflow will always consider the scheduled start time of the covered time interval as the execution_date. Execution_date is a Pendulum object, which is set to the scheduled starting time of the interval that the current run is meant to cover.įor example, in the image below, you can see that a DAG is set to run every hour, starting at 00 and the first run would start at 01 but its execution date will be 00 which is the scheduled start time of the interval that it is meant to cover. The main place of confusion is the execution_date variable. The run for a time interval (chosen based on schedule) will start after that time interval has passed. In Apache Airflow you can specify the starting day for a DAG and the schedule with which you want it to run. INSERT INTO sample.input_data(input_text, datetime_created) You can follow along without setting up your own Airflow instance as well. We will be running a simple example using Apache Airflow and see how we can run a backfill on an already processed dataset. You can visualize the backfill process as shown below. How can I manipulate my execution_date using airflow macros ? How can I modify my SQL query to allow for Airflow backfills ? Most ETL orchestration frameworks provide support for backfilling.
you might realize that there is an error with your processing logic and want to reprocess already processed data.a change in some business logic may need to be applied to an already processed dataset.This is a common use case in data engineering. Backfilling refers to any process that involves modifying or adding new data to existing records in a dataset.
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