What is dynamic task mapping in Airflow?

Dynamic task mapping, introduced in Airflow 2.3, allows you to create a variable number of task instances at runtime based on data rather than defining a fixed set of tasks at DAG parse time.

from airflow.decorators import dag, task

@dag(schedule='@daily', start_date=datetime(2024, 1, 1), catchup=False)
def dynamic_example():
    @task
    def process(item: str):
        print(f'Processing {item}')

    # Creates one task instance per item in the list
    process.expand(item=['a', 'b', 'c', 'd'])

dynamic_example()

You can also map over the output of an upstream task with .expand(item=upstream_task()), which makes the parallelism truly data-driven.

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