airflow 命令

常用示例

说明

Apache Airflow is a platform for programmatically authoring, scheduling, and monitoring workflows. The CLI provides comprehensive control over DAGs (Directed Acyclic Graphs), tasks, connections, and the Airflow services. Workflows are defined as Python code, creating DAGs that describe how tasks should be organized and executed. The scheduler triggers tasks based on defined schedules and dependencies, while the web interface provides monitoring and manual intervention capabilities. The tool manages connections to external systems (databases, APIs, cloud services) and variables for configuration. Resource pools allow controlling task concurrency. The database stores metadata about DAG runs, task states, and history. Common workflows include initializing the database with db migrate, starting the scheduler and webserver, and using dags trigger to manually start DAG runs. Tasks can be tested individually without affecting production state using tasks test.

参数

scheduler

Start the Airflow scheduler daemon to trigger DAG runs

webserver

Start the Airflow web interface server

triggerer

Start the async trigger service for deferrable operators

dags

Manage DAGs (list, trigger, pause, unpause, test, delete, backfill)

tasks

Manage and test individual tasks (run, test, clear, list, render)

db

Database operations (migrate, reset, clean, check, shell)

connections

Manage connection configurations (add, delete, list, export, import)

variables

Manage Airflow variables (get, set, delete, list, export, import)

pools

Manage resource pools for task concurrency control

users

Manage Airflow users (create, delete, list)

config

View and manage configuration settings

providers

Display information about installed providers

info

Show system and environment information

version

Display Airflow version

-o, --output _format_

Output format: table, json, yaml, plain

-v, --verbose

Enable verbose logging