官方文档文档：
http://airflow.incubator.apache.org/project.htmlhtml

1.环境准备

1.1 安装环境

centos 6.7 (docker)
python 2.7.13

docker run --name airflow -h airflow -dti --net hadoopnet --ip=172.18.0.20 -p 10131:22 -v /dfs/centos/airflow/home:/home -v /dfs/centos/airflow/opt:/opt yangxw/centos:6.7python

1.2 建立用户

[root@airflow ~]# groupadd airflow
[root@airflow ~]# useradd airflow -g airflow

2.安装airflow

2.1 安装python

官网只有source包，因此必须编译安装。
参考：编译安装python2.7.13
因为编译python须要升级gcc,进而须要编译gcc,太复杂,所以直接下载python的集成环境Anaconda便可.
wegt https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/mysql

2.2 安装pip

anacconda中集成了pip,直接使用便可.git

2.3 安装数据库

airflow支持mysql postgrey oracle等。这里postgrey.使用yum install postgrey安装便可.github

2.4 安装airflow

airflow组件能够模块化安装，用到哪一个组件安装哪一个组件,如：
web

2.4.1 安装主模块

安装主模块redis

[airflow@airflow ~]$ pip install airflow

2.4.2 安装数据库模块、密码模块

[airflow@airflow ~]$ pip install "airflow[postgres,password]"

2.5 配置airflown

2.5.1 设置环境变量

先设置$AIRFLOW_HOME环境变量。首次执行airflow命令时，会在$AIRFLOW_HOME下面建立airflow的配置文件airflow.cfg。sql

[airflow@airflow ~]$ vi .bashrc
export AIRFLOW_HOME=/home/airflow/airflow01

[airflow@airflow ~]$ airflow
[2017-05-08 02:00:04,677] {__init__.py:57} INFO - Using executor SequentialExecutor
usage: airflow [-h]
               {resetdb,render,variables,connections,pause,task_failed_deps,version,trigger_dag,initdb,test,unpause,dag_state,run,list_tasks,backfill,list_dags,kerberos,worker,webserver,flower,scheduler,task_state,pool,serve_logs,clear,upgradedb}
               …
airflow: error: too few arguments
[airflow@airflow ~]$ ll airflow01/
total 16
-rw-rw-r-- 1 airflow airflow 11418 May  8 02:00 airflow.cfg
-rw-rw-r-- 1 airflow airflow  1549 May  8 02:00 unittests.cfg

2.5.2 修改配置文件

查看airflow.cfg文件，整个文件分为core、cli、api、operators、webserver、email、smtp、celery、scheduler、mesos、kerberos、github_enterprise、admin几个部分。
对其中一些参数作修改，其它的保持默认值便可：docker

[core]
airflow_home = /home/airflow/airflow01
dags_folder = /home/airflow/airflow01/dags #dag python文件目录 
executor = LocalExecutor #先使用local模式
base_log_folder = /home/airflow/airflow01/logs #主日志目录
sql_alchemy_conn = postgresql+psycopg2://yangxiaowen:yangxiaowen@10.38.1.78:5432/yangxiaowen
load_examples = True
default_impersonation = xiaowen.yang
[webserver]
authenticate = True
auth_backend = airflow.contrib.auth.backends.password_auth #1.8.1版本中cfg文件没有写这个参数,必定要加上,否则会报"airflow.exceptions.AirflowException: Failed to import authentication backend"错误
filter_by_owner = true
web_server_host = XXX.XXX.XXX.XXX  #web server 机器IP
base_url = http://XXX.XXX.XXX.XXX:8080  #web server 机器IP:PORT
[smtp]
smtp_host = smtp.exmail.qq.com
smtp_user = bd-no-reply@bqjr.cn
smtp_password = BQJRbd@2016
smtp_mail_from = bd-no-reply@bqjr.cn

3. 启动airflow

3.1 初始化数据库

[airflow@airflow ~]$ airflow initdb

3.2 建立用户

$ python
Python 2.7.9 (default, Feb 10 2015, 03:28:08)
Type "help", "copyright", "credits" or "license" for more information.
>>> import airflow
>>> from airflow import models, settings
>>> from airflow.contrib.auth.backends.password_auth import PasswordUser
>>> user = PasswordUser(models.User())
>>> user.username = 'new_user_name'
>>> user.email = 'new_user_email@example.com'
>>> user.password = 'set_the_password'
>>> session = settings.Session()
>>> session.add(user)
>>> session.commit()
>>> session.close()
>>> exit()

3.3 启动airflow

[airflow@airflow ~]$ airflow webserver -p 8080

[airflow@airflow ~]$ airflow scheduler

若是不出错就启动成功了.
能够在页面上查看airflow的页面.
数据库

4.执行任务

airflow中的任务都是python程序.下面建立一个简单的python程序.
在$AIRFLOW_HOME下建立dags\logs目录.

vi testBashOperator.py
#!/usr/bin/python
from airflow import DAG
from airflow.operators.bash_operator import BashOperator
from datetime import datetime, timedelta

default_args = {
    'owner': 'yangxw',
    'depends_on_past': False,
    'start_date': datetime(2017, 5, 9),
    'email': ['xiaowen.yang@bqjr.cn'],
    'email_on_failure': True,
    'email_on_retry': True,
    'retries': 1,
    'retry_delay': timedelta(minutes=5),
    # 'queue': 'bash_queue',
    # 'pool': 'backfill',
    # 'priority_weight': 10,
    # 'end_date': datetime(2016, 1, 1),
}

dag = DAG('testBashOperator', 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)

t2.set_upstream(t1)

airflow webserver --debug=True

执行 python testBashOperator.py编译该文件,而后执行 airflow run testBashOperator print_date 2017-05-09 执行文件,在页面上能看到dag信息.

5.安装celery

celery是一个分布式消息队列,在airflow中,使用celeryExecutor能够动态的增长worker个数并将任务在远程机器上执行.生产中建议使用celeryExecutor来执行.

5.1 安装celery模块

pip install airflow[celery]

5.2 安装celery broker

celery须要设置broker和result队列(能够用一样的)来保存消息.celery 支持多种broker:

5.2.1 使用RabbitMQ做为broker

安装airflow的RabbitMQ模块
celery可使用RabbitMQ或者redias等作为broker,甚至可使用一些Experimental(实验性的)工具(如sqlalchemy支持的数据库),默认使用RabbitMQ.
pip install airflow[rabbitmq]
安装RabbitMQ-server
yum install rabbitmq-server
(有160多个依赖包!)
而后启动service rabbitmq-server start
配置 rabbitmq
http://blog.csdn.net/qazplm12_3/article/details/53065654

rabbitmqctl add_user ct 152108
rabbitmqctl add_vhost ct_airflow
rabbitmqctl set_user_tags ct airflow
rabbitmqctl set_permissions -p ct_airflow ct ".*" ".*" ".*"

5.2.2 使用Redis作为broker

安装celery redis模块
pip install -U "celery[redis]"
安装redis数据库
yum install redis
启动redis
service redis start
4.修改airflow配置文件
broker_url = redis://localhost:6379/0
celery_result_backend = redis://localhost:6379/0

5.3 修改airflow配置文件启用celery

修改airflow.cfg文件:
[core]
executor = CeleryExecutor
[celery]
broker_url = amqp://ct:152108@localhost:5672/ct_airflow
celery_result_backend = amqp://ct:152108@localhost:5672/ct_airflow

5.4 测试celery

[airflow@airflow ~]$ airflow webserver -p 8100
[airflow@airflow ~]$ airflow scheduler
[airflow@airflow ~]$ airflow worker  #启动celeryexcutor

能够看到CeleryExecutor启动状况.再执行airflow run testBashOperator print_date 2017-05-09,看看CeleryExecutor运行状况.

5.5 部署多个worker

在须要运行做业的机器上的安装airflow airflow[celery] celery[redis] 模块后,启动airflow worker便可.这样做业就能运行在多个节点上.

6. 问题

在docker中遇到如下问题,换成实体机后解决

[2017-05-10 09:14:59,777: ERROR/Worker-1] Command 'airflow run testFile echoDate 2017-05-10T00:00:00 --local -sd DAGS_FOLDER/testFile.py' returned non-zero exit status 1
[2017-05-10 09:14:59,783: ERROR/MainProcess] Task airflow.executors.celery_executor.execute_command[c5d5ea39-0141-46bb-b33a-06a924c07508] raised unexpected: AirflowException('Celery command failed',)
Traceback (most recent call last):
  File "/opt/anaconda2/lib/python2.7/site-packages/celery/app/trace.py", line 240, in trace_task
    R = retval = fun(*args, **kwargs)
  File "/opt/anaconda2/lib/python2.7/site-packages/celery/app/trace.py", line 438, in __protected_call__
    return self.run(*args, **kwargs)
  File "/opt/anaconda2/lib/python2.7/site-packages/airflow/executors/celery_executor.py", line 59, in execute_command
    raise AirflowException('Celery command failed')
AirflowException: Celery command failed

参考:

http://airflow.incubator.apache.org
http://www.javashuo.com/article/p-xypmfobn-nu.html
http://blog.csdn.net/qazplm12_3/article/details/53065654
http://docs.celeryproject.org/en/latest/getting-started/brokers/index.html
http://www.rabbitmq.com/install-rpm.html

airflow的安装