Learn more about the Apache Airflow Backfill feature. Our Apache Support team is here to help you with your questions and concerns.

About Apache Airflow Backfill Feature

Did you know that Apache Airflow’s backfilling feature helps with the execution of DAGs across historical time periods?

This comes in handy when we need to reprocess data due to changes in the processing logic or recovery from failures.

Here are a few key points about backfilling in Airflow:

• The ‘airflow dags backfill’ command acts as the gateway to start backfill operations. Users can specify start and end dates to define the backfill period.
• Ensuring DAG idempotency is essential. It offers consistent results when executing the same DAG multiple times on identical time periods.
• Airflow manages deleted DAG runs during backfill operations, recreating and rerunning them to maintain workflow continuity and data consistency.
• Designing DAGs with backfilling be careful about task reruns’ impact, particularly regarding external side effects. Thorough planning reduces disruptions to downstream processes.
• Backfilling operations can strain system resources. This needs monitoring and resource allocation adjustments for optimal performance. Airflow’s concurrency controls offer granular management of task parallelization, enhancing operational efficiency.
• Furthermore, we can use Airflow’s concurrency controls to manage the tasks running in parallel during a backfill operation.

For example, we can start a backfill operation as seen here:

airflow dags backfill my_dag \
--start-date 2021-01-01 \
--end-date 2021-01-07

Airflow allows us to choose how to execute the workflow.

If the workflow runs hourly, there is a new run that processes relevant data every hour.

Suppose did not run for 3 hours for some reason. In this case, if we set catchup=True, the airflow will not skip runs. Hence, airflow invokes all three missing runs.

However, if we set catchup=False, the airflow skips the missing runs and invokes one run for the three-hour window.

Here, option 1 and 2 depends on how we write the DAG.

Let us know in the comments if you need help with Airflow’s Backfill feature.

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Conclusion

In brief, our Support Experts introduced us to the Apache Airflow Backfill feature.