airflow-supervisor

Run and monitor supervisord-managed jobs from Apache Airflow.

from airflow import DAG
from airflow_supervisor import ProgramConfiguration, Supervisor, SupervisorAirflowConfiguration

dag = DAG(dag_id="nightly-supervisor", schedule="@daily")
config = SupervisorAirflowConfiguration(
    working_dir="/var/tmp/nightly-supervisor",
    program={
        "nightly": ProgramConfiguration(command="python /opt/jobs/nightly.py"),
    },
)
Supervisor(dag=dag, cfg=config)

The generated lifecycle creates a dedicated supervisord instance, starts and monitors programs with airflow-ha, handles retriggers, and optionally stops and removes the instance. SupervisorSSH manages the same lifecycle remotely.

Documentation

• Tutorial: run a supervised job from Airflow

• How-to guides

• Why Airflow delegates process ownership

• API reference

Published documentation is available at airflow-laminar.github.io/airflow-supervisor.

Ecosystem

• supervisor-pydantic supplies supervisord models and lifecycle tools.

• systemd-pydantic and cron-pydantic model alternative runtimes.

• airflow-systemd provides the analogous systemd lifecycle.

• airflow-cron converts cron jobs into ordinary Airflow tasks.

• airflow-pydantic supplies declarative task, host, and connection models.

• airflow-config produces YAML-driven DAGs.

Note

This library was generated using copier from the Base Python Project Template repository.

Install the Python Templates Copier Update GitHub App to receive weekly template update pull requests.