How to Backfill the Data in Airflow

In Apache Airflow, backfilling is the process of running a DAG or a subset of its tasks for a specific date range in the past. This can be useful if you need to fill in missing data, or if you want to re-run a DAG for a specific period of time to test or debug it.

Here are the steps to backfill a DAG in Airflow:

1. Navigate to the Airflow web UI and select the DAG that you want to backfill.
2. In the DAG detail view, click on the "Graph View" tab.
3. Click on the "Backfill" button in the top right corner of the page.
4. In the "Backfill Job" form that appears, specify the date range that you want to backfill. You can use the "From" and "To" fields to set the start and end dates, or you can use the "Last X" field to backfill a certain number of days.
5. Optional: If you want to backfill only a subset of the tasks in the DAG, you can use the "Task Instances" field to specify a comma-separated list of task IDs.
6. Click on the "Start" button to start the backfill job.

The backfill job will run asynchronously in the background. You can monitor its progress by navigating to the "Task Instances" tab in the DAG detail view, or by checking the "Backfill" tab in the Airflow UI.

It's worth noting that backfilling a DAG can be resource-intensive, especially if you are running a large number of tasks or a long date range. You should be careful not to overburden your Airflow cluster when backfilling. You can use the "Dry Run" option to test the backfill job without actually running any tasks, or you can use the "SubDAG" feature to break up a large DAG into smaller, more manageable chunks.

Here is an example of how you can backfill a DAG in Python using the Airflow API:

import airflow
from airflow.models import DAG, DagRun, TaskInstance

# Set the start and end dates for the backfill
start_date = "2022-01-01"
end_date = "2022-01-03"

# Set the DAG ID and task IDs for the tasks you want to backfill
dag_id = "my_dag"
task_ids = ["task_1", "task_2"]

# Create a DAG object
dag = DAG.get_dag(dag_id)

# Create a DagRun object for the backfill
run_id = f"manual__{start_date}__{end_date}"
dag_run = DagRun(
    dag_id=dag_id,
    run_id=run_id,
    start_date=start_date,
    end_date=end_date,
    execution_date=start_date,
    state=State.RUNNING,
    external_trigger=True,
)

# Create a list of TaskInstance objects for the backfill
task_instances = []
for task_id in task_ids:
    ti = TaskInstance(task=dag.get_task(task_id), execution_date=start_date)
    task_instances.append(ti)

# Run the backfill
backfill_job = BackfillJob(
    dag=dag,
    start_date=start_date,
    end_date=end_date,
    mark_success=False,
    dag_run=dag_run,
    task_instances=task_instances,
)
backfill_job.run()