Airflow provides a built‑in object storage backend in the common‑io provider package. To enable it, set the xcom_backend configuration:
Example (Python using redis-py):
Because XCom values can be used as inputs to downstream tasks, you can create dynamic pipelines where the number or configuration of tasks is determined by runtime data. This is a powerful pattern for scenarios such as: airflow xcom exclusive
To ensure your Airflow deployment handles state transfer efficiently, securely, and exclusively, adhere to these production practices: Implementation Custom XCom Backend Route payloads to S3/GCS; store references in the DB. Data Isolation Multi-tenant scoping Set include_prior_dates=False on critical pulls. Scale Tasks Dynamic Task Mapping
[Task A] ---> (Returns Data) ---> [Custom XCom Backend] ---> Uploads to Cloud Storage (S3/GCS) | v Stores URI Reference in Airflow Metadata DB Step-by-Step Custom Backend Implementation Airflow provides a built‑in object storage backend in
Example with Redis:
Add reliability:
In a downstream task, you pull the value:
from datetime import datetime from airflow import DAG from airflow.operators.python import PythonOperator def push_function(**kwargs): kwargs['ti'].xcom_push(key='model_accuracy', value=0.94) def pull_function(**kwargs): ti = kwargs['ti'] accuracy = ti.xcom_pull(task_ids='push_task', key='model_accuracy') print(f"Model accuracy is accuracy") with DAG('legacy_xcom_dag', start_date=datetime(2026, 1, 1), schedule=None) as dag: push_task = PythonOperator(task_id='push_task', python_callable=push_function) pull_task = PythonOperator(task_id='pull_task', python_callable=pull_function) push_task >> pull_task Use code with caution. The TaskFlow API Approach store references in the DB.