What’s the best way to handle having a different schedule interval for backfilling and ongoing running?
For backfilling I want to use a daily interval, but for ongoing running I want to use an hourly interval.
I can think of three approaches to this:
The easiest approach I see is to define two DAGs in the one .py file. dag_backfill with a daily interval, a start date in the past and end date of datetime.now(), and dag_ongoing with an hourly interval and start date of datetime.now() that takes over when dag_backfill finishes. However two DAGs in one file is discouraged here:
We do support more than one DAG definition per python file, but it is not recommended as we would like better isolation between DAGs from a fault and deployment perspective…
Two .py files that import the same python functions that make up the pipeline. I worry about keeping the separate files consistent in this approach.
Only one DAG with an hourly interval that checks if the the run date is over 1 day in the past and if so only runs at midnight for those dates. I feel like that is inelegant through as it would obscure the schedule the backfilling will run on, at least from the gui homepage.
Is there a common pattern for this or known best practice?
Okay, here\’s a breakdown of the problem and the best approach to solve it, keeping in mind Airflow best practices and maintainability.\n\n**Understanding the Problem**\n\nThe core issue is needing two different schedules for the *same* data pipeline:\n\n* **Backfilling:** Run over a historical period, ideally at a coarser granularity (daily) for speed and resource efficiency.\n* **Ongoing (Production):** Run regularly (hourly) to process new data.\n\nThe constraints include:\n\n* Discouragement of multiple DAGs within a single Python file (for isolation).\n* The need to maintain consistency between the backfill and ongoing pipelines.\n* Desire for clear visibility of the backfill schedule.\n\n**Why Your Initial Approaches Fall Short**\n\n1. **Two DAGs in One File:** As mentioned, this violates the recommendation for DAG isolation. If one DAG has an issue, it could potentially impact the other during parsing or execution.\n\n2. **Two Separate Files Importing the Same Functions:** While this addresses the isolation concern, it introduces a significant maintenance burden. Any change to the core pipeline logic needs to be carefully propagated to both files, increasing the risk of inconsistencies and bugs. This approach is essentially code duplication.\n\n3. **Single Hourly DAG with Conditional Execution:** This is problematic for several reasons:\n\n * **Obscured Schedule:** The Airflow UI won\’t accurately reflect the backfill schedule, making it harder to monitor and manage.\n * **Inefficient:** The DAG will wake up every hour, even if it only needs to do work once a day during the backfill period.\n * **Complexity:** The conditional logic within the DAG to determine when to execute adds unnecessary complexity.\n\n**The Recommended Solution: A Hybrid Approach with External Task Sensors and DAG Generation**\n\nThis approach balances isolation, maintainability, and clarity:\n\n1. **Core Pipeline Logic in a Reusable Module:**\n\n * Create a separate Python module (e.g., `my_pipeline.py`) that contains *only* the task definitions and core data processing logic. This module should **not** define any DAGs. It should only contain functions or classes that create your tasks.\n * These functions should be parameterized so they can be easily configured for different date ranges or other backfill/ongoing settings.\n\n “`python\n # my_pipeline.py\n\n from airflow.operators.python import PythonOperator\n from airflow.models import Variable\n from datetime import datetime, timedelta\n\n def create_process_data_task(dag, execution_date):\n \”\”\”Creates a task to process data for a given date.\”\”\”\n task = PythonOperator(\n task_id=f\”process_data_{execution_date.strftime(\’%Y%m%d\’)}\”, # Unique task ID\n dag=dag,\n python_callable=process_data, # Your data processing function\n op_kwargs={\’date\’: execution_date}\n )\n return task\n\n\n def process_data(date):\n \”\”\”\n Your core data processing logic.\n \”\”\”\n # Connect to database, read data, transform, load, etc.\n print(f\”Processing data for {date}\”)\n # Example:\n # data = get_data_from_source(date)\n # transformed_data = transform_data(data)\n # load_data_to_destination(transformed_data)\n\n “`\n\n2. **`backfill_dag.py`:**\n\n * Create a DAG specifically for backfilling. This DAG will have a `start_date` in the past and use a `schedule_interval=\’@daily\’`.\n * Use a loop to dynamically generate tasks for each day in the backfill range. Import the `create_process_data_task` function from `my_pipeline.py` to instantiate each task.\n\n “`python\n # backfill_dag.py\n\n from airflow import DAG\n from datetime import datetime, timedelta\n from my_pipeline import create_process_data_task\n\n default_args = {\n \’owner\’: \’airflow\’,\n \’depends_on_past\’: False,\n \’start_date\’: datetime(2023, 1, 1), # Start of backfill period\n \’email_on_failure\’: False,\n \’email_on_retry\’: False,\n \’retries\’: 1,\n \’retry_delay\’: timedelta(minutes=5),\n }\n\n with DAG(\n dag_id=\’backfill_pipeline\’,\n default_args=default_args,\n schedule_interval=\’@daily\’, # Run daily\n catchup=True, # Important for backfills\n tags=[\’backfill\’]\n ) as dag_backfill:\n\n start_date = dag_backfill.default_args[\’start_date\’]\n end_date = datetime.now()\n\n current_date = start_date\n while current_date <= end_date:\n process_task = create_process_data_task(dag_backfill, current_date)\n current_date += timedelta(days=1)\n ```\n\n3. **`ongoing_dag.py`:**\n\n * Create a separate DAG for ongoing processing. This DAG will have a `start_date` of `datetime.now()` (or a recent time) and use a `schedule_interval=\'@hourly\'`.\n * Import the `create_process_data_task` function from `my_pipeline.py` to create the main processing task.\n\n ```python\n # ongoing_dag.py\n\n from airflow import DAG\n from airflow.sensors.external_task import ExternalTaskSensor\n from datetime import datetime, timedelta\n from my_pipeline import create_process_data_task\n\n default_args = {\n \'owner\': \'airflow\',\n \'depends_on_past\': False,\n \'start_date\': datetime.now() - timedelta(hours=1), # Start now (or a bit before)\n \'email_on_failure\': False,\n \'email_on_retry\': False,\n \'retries\': 1,\n \'retry_delay\': timedelta(minutes=5),\n }\n\n with DAG(\n dag_id=\'ongoing_pipeline\',\n default_args=default_args,\n schedule_interval=\'@hourly\', # Run hourly\n catchup=False, # Don\'t run for past missed runs. Essential.\n tags=[\'ongoing\']\n ) as dag_ongoing:\n execution_date = \'{{ execution_date }}\'\n process_task = create_process_data_task(dag_ongoing, execution_date)\n\n ```\n\n**Explanation and Benefits**\n\n* **Code Reusability:** The core pipeline logic is defined once in `my_pipeline.py` and reused by both DAGs. This dramatically reduces the risk of inconsistencies and simplifies maintenance. If you change the way you process data, you only need to change it in *one* place.\n* **DAG Isolation:** Each DAG is in its own file, adhering to the recommended practice for fault isolation.\n* **Clear Schedules:** The Airflow UI clearly shows the daily backfill schedule and the hourly ongoing schedule.\n* **Dynamic Task Generation:** The backfill DAG uses a loop to generate tasks for each day, making it easy to define the backfill range. The `catchup=True` argument in the backfill DAG is essential to ensure that it processes all the historical dates. The `catchup=False` argument in the ongoing dag will make sure it doesn\'t start filling in the past missed runs.\n* **Clean Separation of Concerns:** Each DAG is responsible for its specific purpose (backfilling or ongoing processing), making the code more understandable and maintainable.\n\n**Important Considerations**\n\n* **Task IDs:** Ensure that task IDs are unique across *all* DAGs in your Airflow environment. The `process_data_{execution_date.strftime(\'%Y%m%d\')}` task id pattern in the backfill DAG helps with this.\n* **Error Handling:** Implement robust error handling within the `process_data` function. Consider using Airflow\'s retry mechanism and alerting.\n* **Resource Management:** Be mindful of resource usage during backfilling. You might need to adjust the number of parallel tasks to avoid overwhelming your system. Consider using Airflow pools or queuing mechanisms to manage resource allocation.\n* **Testing:** Write unit tests for the functions in `my_pipeline.py` to ensure the core logic is correct. Test the backfill and ongoing DAGs separately to verify their scheduling and task execution.\n* **Monitoring:** Set up monitoring to track the progress of both the backfill and ongoing pipelines. Use Airflow\'s built-in monitoring tools or integrate with external monitoring systems.\n\nThis comprehensive approach provides a robust, maintainable, and scalable solution for handling different schedules for backfilling and ongoing data pipelines in Airflow. It prioritizes code reuse, DAG isolation, and clear visibility of schedules, all of which are essential for building reliable data workflows.\n
