Airflow开发指南
Airflow是一个工作流分配管理系统，它通过有向无环图的方式管理任务流程，可以设置任务依赖关系和时间调度。Airflow服务会在master1上启动2个服务，AirflowScheduler与AirflowWebserver。AirflowScheduler用于管理所有DAGs、Tasks和Tasks之间的调度，AirflowWebserver是Airflow的web服务，用于可视化管理。
如果您创建集群勾选了Airflow，Airflow将被安装在uhadoop-******-master1节点上。访问Airflow Web服务通过master1节点外网IP:8999访问（需要您开放master1节点绑定的外网防火墙8999端口）
1. Airflow示例
详情请参考官网介绍
请将如下代码放置于uhadoop-******-master2的/home/hadoop/airflow/dags目录下
如无此目录，则需手动创建，并修改用户组为hadoop
cat tutorial.py
python
"""
Code that goes along with the Airflow tutorial located at:
https://github.com/airbnb/airflow/blob/master/airflow/example_dags/tutorial.py
"""
from airflow import DAG
from airflow.operators.bash_operator import BashOperator
from datetime import datetime, timedelta
default_args = {
'owner': 'airflow',
'depends_on_past': False,
'start_date': datetime(2015, 6, 1),
'email': ['airflow@airflow.com'],
'email_on_failure': False,
'email_on_retry': False,
'retries': 1,
'retry_delay': timedelta(minutes=5),
# 'queue': 'bash_queue',
# 'pool': 'backfill',
# 'priority_weight': 10,
# 'end_date': datetime(2016, 1, 1),
}
dag = DAG('tutorial', default_args=default_args)
# t1, t2 and t3 are examples of tasks created by instantiating operators
t1 = BashOperator(
task_id='print_date',
bash_command='date',
dag=dag)
t2 = BashOperator(
task_id='sleep',
bash_command='sleep 5',
retries=3,
dag=dag)
templated_command = """
{% for i in range(5) %}
echo "{{ ds }}"
echo "{{ macros.ds_add(ds, 7)}}"
echo "{{ params.my_param }}"
{% endfor %}
"""
t3 = BashOperator(
task_id='templated',
bash_command=templated_command,
params={'my_param': 'Parameter I passed in'},
dag=dag)
t2.set_upstream(t1)
t3.set_upstream(t1)
页面刷新后将会存在 tutorial DAG
详细使用方法请参考Airflow官网网站