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Need help integrating Mage into an existing Airflow project? Check out this tutorial or get instant help from us in Slack.
We support running the pipeline in Airflow DAGs.
  • You need to firstly install mage_ai library by adding mage_ai to your requirements.txt file.
  • Then you need to download the mage pipeline code into your Airflow directory. You can achieve it by using a git submodule in your Airflow directory.
  • In your Mage project’s metadata.yaml file, please specify a variables_dir that you want to store the output of each block execution (You need to have write permission to the variables_dir). Example: variables_dir: /tmp.
We provide multiple ways to run mage pipelines in Airflow.

​
Create DAGs for all the pipelines in Mage project

from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from datetime import datetime
from mage_ai.orchestration.airflow import create_dags
import os
ABSOLUTE_PATH = os.path.abspath(os.path.dirname(__file__))
project_path = os.path.join(ABSOLUTE_PATH, 'project_path')
create_dags(
    project_path,
    DAG,
    PythonOperator,
    blacklist_pipelines=[],     # Blacklisted pipeline uuids
    dag_settings=dict(
        start_date=datetime(2022, 8, 2),
    ),
    globals_dict=globals(),
)

​
Run pipeline in a BashOperator

Example code: 
from airflow import DAG
from airflow.operators.bash_operator import BashOperator
from datetime import datetime
import os
ABSOLUTE_PATH = os.path.abspath(os.path.dirname(__file__))
project_path = os.path.join(ABSOLUTE_PATH, 'project_path')
pipeline_name = 'pipline_name'
dag = DAG(
    'test_mage_pipeline',
    start_date=datetime(2022, 7, 14),
    description='Test running mage pipeline.',
    schedule_interval='@once',
    catchup=False,
)
task = BashOperator(
    dag=dag,
    task_id='run_pipeline',
    bash_command=f'mage run {project_path} {pipeline_name}',
)

​
Run pipeline in a PythonOperator

Example code: 
from airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime
import mage_ai
import os
ABSOLUTE_PATH = os.path.abspath(os.path.dirname(__file__))
project_path = os.path.join(ABSOLUTE_PATH, 'project_path')
pipeline_name = 'pipline_name'
dag = DAG(
    'test_mage_pipeline',
    start_date=datetime(2022, 7, 14),
    description='Test running mage pipeline.',
    schedule_interval='@once',
    catchup=False,
)
def build_execute_pipeline(project_path, pipeline_name):
    def _callable(ds, **kwargs):
        mage_ai.run(pipeline_name, project_path)
    return _callable
task = PythonOperator(
    dag=dag,
    task_id='run_pipeline',
    python_callable=build_execute_pipeline(project_path, pipeline_name),
)

​
Run pipeline as an Airflow DAG

Example code: 
from airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime
from mage_ai.data_preparation.models.pipeline import Pipeline
from mage_ai.shared.hash import ignore_keys
import mage_ai
import os
ABSOLUTE_PATH = os.path.abspath(os.path.dirname(__file__))
project_path = os.path.join(ABSOLUTE_PATH, 'project_path')
pipeline_name = 'pipline_name'
dag = DAG(
    'test_mage_pipeline',
    start_date=datetime(2022, 7, 14),
    description='Test running mage pipeline.',
    schedule_interval='@once',
    catchup=False,
)
pipeline = Pipeline(pipeline_name, repo_path=project_path)
tasks = []
def build_execute_block(block):
    def _callable(ds, **kwargs):
        mage_ai.run(
            pipeline_name,
            project_path=project_path,
            block_uuid=block.uuid,
            analyze_outputs=False,
            update_status=False,
        )
    return _callable
for uuid, b in pipeline.blocks_by_uuid.items():
    if b.type == 'scratchpad':
        continue
    tasks.append(dict(
        task_id=uuid,
        upstream_task_ids=b.upstream_block_uuids,
        python_callable=build_execute_block(b),
    ))
def initialize_tasks(dag, tasks):
    operators = {}
    for task_dict in tasks:
        task_operator = PythonOperator(dag=dag, **ignore_keys(task_dict, [
            'upstream_task_ids',
        ]))
        operators[task_operator.task_id] = task_operator
    for task_dict in tasks:
        for task_id in task_dict.get('upstream_task_ids', []):
            dag.set_dependency(task_id, task_dict['task_id'])
    return operators
initialize_tasks(dag, tasks)