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.. _sdk-dynamic-task-mapping:

Dynamic Task Mapping with Task SDK

Dynamic Task Mapping allows tasks defined with the Task SDK to generate a variable number of task instances at runtime based on upstream data. This is enabled via the expand() method on tasks, providing a way to parallelize execution without knowing the number of tasks ahead of time.

Simple Mapping

Map over a Python list directly in the DAG:

.. code-block:: python

from datetime import datetime

from airflow.sdk import DAG, task

@task def add_one(x: int): return x + 1

@task def sum_it(values: list[int]): print(f"Total was {sum(values)}")

with DAG(dag_id="dynamic-map-simple", start_date=datetime(2022, 1, 1)) as dag: summed = sum_it(values=add_one.expand(x=[1, 2, 3, 4, 5]))

Task-Generated Mapping

Generate the list at runtime from an upstream task:

.. code-block:: python

@task def make_list(): # This could fetch data from an API, database, etc. return ["a", "b", "c"]

@task def consume(item: str): print(item)

with DAG(dag_id="dynamic-map-generated", start_date=datetime(2022, 1, 1)) as dag: consume.expand(item=make_list())

Details

• Only keyword arguments can be passed to expand().

• Mapped inputs are provided to tasks as lazy proxy objects. To force evaluation into a concrete list, wrap the proxy in list().

• Combine static parameters with mapped ones using partial():

  .. code-block:: python

  @task def add(x: int, y: int): return x + y

  with DAG(dag_id="map-with-partial", start_date=datetime(2022, 1, 1)) as dag: add.partial(y=10).expand(x=[1, 2, 3])

Advanced Usage

For advanced patterns—such as repeated mapping, cross-product mapping, named mappings (via map_index_template), and handling large datasets—see the Airflow Core documentation:

Dynamic Task Mapping in the Airflow Core docs <https://airflow.apache.org/docs/apache-airflow/stable/authoring-and-scheduling/dynamic-task-mapping.html>_.