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Airflow能做什么

Airflow是一个工作流分配管理系统。

安装和使用

  • 最简单安装

    • pip install airflow
    • pip install "airflow[crypto, password]"
  • 安装成功之后,就可以使用了,默认使用SequentialExecutor, 顺次执行任 务。

    • 初始化数据库 airflow initdb
    • 启动web服务器 airflow webserver -p 8080
    • 启动任务 airflow scheduler
    • 或者测试文章末尾的DAG airflow test ct1 print_date 2016-05-14

配置 mysql以启用LocalExecutor

  • 安装mysql数据库支持
    • yum install mysql mysql-server
    • pip install airflow[mysql]
  • 设置mysql根用户的密码
    • mysql -uroot登录mysql数据库
    • 在mysql操作界面依次输入sql语句

      SET PASSWORD=PASSWORD("passwd");

      FLUSH PRIVILEGES;

  • 新建用户和数据库
    • 新建名字为airflow的数据库 CREATE DATABASE airflow;
    • 新建用户ct,密码为152108, 该用户对数据网airflow有完全操作 权限

      GRANT all privileges on airflow.* TO 'ct'@'localhost' IDENTIFIED BY '152108';

      FLUSH PRIVILEGES;

    • 新建数据库celery_result_airflow用于celery_result_backend (这一步后来没用到,可以略过)

      mysql -uct -p152108

      CREATE DATABASE celery_result_airflow;

  • 修改airflow配置文件支持mysql
    • airflow.cfg 文件通常在~/airflow目录下
    • 更改数据库链接:

      sql_alchemy_conn = mysql://ct:152108@localhost/airflow

      对应字段解释如下:

      dialect+driver://username:password@host:port/database

    • 初始化数据库 airflow initdb
    • 初始化数据库成功后,可进入mysql查看新生成的数据表。

      mysql -uct -p152108

      USE airflow;

      SHOW TABLES;

  • centos7中使用mariadb取代了mysql, 但所有命令的执行相同

    yum install mariadb mariadb-server

    systemctl start mariadb ==> 启动mariadb

    systemctl enable mariadb ==> 开机自启动

    mysql_secure_installation ==> 设置 root密码等相关

    mysql -uroot -p123456 ==> 测试登录!

使用LocalExecutor (可选方案一)

配置LocalExecutor

  • 修改airflow配置文件

    • airflow.cfg 文件通常在~/airflow目录下

    • 更改executor为 executor = LocalExecutor

测试

  • ~/airflow/dags下新建几个task文件,具体见TASK
  • 顺次执行下面的命令
    • airflow initdb (若前面执行过,就跳过)
    • airflow webserver --debug &
    • airflow scheduler
  • 打开网页查看运行进程

使用CeleryExecutor (可选方案二)

配置celery+rabbitmq支持

  • 安装celery和rabbitmq
    • pip install airflow[celery]
    • pip install airflow[rabbitmq]

    • 安装erlang和rabbitmq (Centos 6,REF):

      wget ~centos~6_amd64.rpm

      yum install esl-erlang_18.3-1~centos~6_amd64.rpm

      wget https://github.com/jasonmcintosh/esl-erlang-compat/releases/download/1.1.1/esl-erlang-compat-18.1-1.noarch.rpm

      yum install esl-erlang-compat-18.1-1.noarch.rpm

      wget

      yum install rabbitmq-server-3.6.1-1.noarch.rpm

  • 配置rabbitmq

    • 启动rabbitmq: rabbitmq-server -detached
    • 开机启动rabbitmq: chkconfig rabbitmq-server on
    • 配置rabbitmq (REF)

      rabbitmqctl add_user ct 152108

      rabbitmqctl add_vhost ct_airflow

      rabbitmqctl set_user_tags ct airflow

      rabbitmqctl set_permissions -p ct_airflow ct “.” ”.” ”.*”

      rabbitmq-plugins enable rabbitmq_management # no usage

  • 修改airflow配置文件支持Celery

    • airflow.cfg 文件通常在~/airflow目录下

    • 更改executor为 executor = CeleryExecutor

    • 更改broker_url

      broker_url = amqp://ct:152108@localhost:5672/ct_airflow

      Format explanation: transport://userid:password@hostname:port/virtual_host

    • 更改celery_result_backend,

      celery_result_backend = amqp://ct:152108@localhost:5672/ct_airflow

      Format explanation: transport://userid:password@hostname:port/virtual_host

      可以与broker_url相同

测试

  • 启动服务器:airflow webserver --debug
  • 启动celery worker (不能用根用户):airflow worker
  • 启动scheduler: airflow scheduler
  • 提示:
    • 测试过程中注意观察运行上面3个命令的3个窗口输出的日志
    • 当遇到不符合常理的情况时考虑清空 airflow backend的数据库, 可使用airflow resetdb清空。
    • 删除dag文件后,webserver中可能还会存在相应信息,这时需要重启 webserver。

    关闭webserver: ps -ef|grep -Ei '(airflow-webserver)'| grep master | awk '{print $2}'|xargs -i kill {}

一个脚本控制airflow系统的启动和重启

#!/bin/bash

#set -x
#set -e
set -u

usage()
{
cat <<EOF
${txtcyn}
Usage:

$0 options${txtrst}

${bldblu}Function${txtrst}:

This script is used to start or restart webserver service.

${txtbld}OPTIONS${txtrst}:
	-S	Start airflow system [${bldred}Default FALSE${txtrst}]
	-s	Restart airflow server only [${bldred}Default FALSE${txtrst}]
	-a	Restart all airflow programs including webserver, worker and
		scheduler. [${bldred}Default FALSE${txtrst}]
EOF
}

start_all=
server_only=
all=

while getopts "hs:S:a:" OPTION
do
	case $OPTION in
		h)
			usage
			exit 1
			;;
		S)
			start_all=$OPTARG
			;;
		s)
			server_only=$OPTARG
			;;
		a)
			all=$OPTARG
			;;
		?)
			usage
			exit 1
			;;
	esac
done

if [ -z "$server_only" ] && [ -z "$all" ] && [ -z "${start_all}" ]; then
	usage
	exit 1
fi

if [ "$server_only" == "TRUE" ]; then
	ps -ef | grep -Ei '(airflow-webserver)' | grep master | \
		awk '{print $2}' | xargs -i kill {}
	cd ~/airflow/
	nohup airflow webserver >webserver.log 2>&1 &
fi

if [ "$all" == "TRUE" ]; then
	ps -ef | grep -Ei 'airflow' | grep -v 'grep' | awk '{print $2}' | xargs -i kill {}
	cd ~/airflow/
	nohup airflow webserver >>webserver.log 2>&1 &
	nohup airflow worker >>worker.log 2>&1 &
	nohup airflow scheduler >>scheduler.log 2>&1 &
fi


if [ "${start_all}" == "TRUE" ]; then
	cd ~/airflow/
	nohup airflow webserver >>webserver.log 2>&1 &
	nohup airflow worker >>worker.log 2>&1 &
	nohup airflow scheduler >>scheduler.log 2>&1 &
fi

airflow.cfg 其它修改

  • dags_folder

    dags_folder目录支持子目录和软连接,因此不同的dag可以分门别类的存储 起来。

  • 设置邮件发送服务

    smtp_host =

    smtp_starttls = True

    smtp_ssl = False

    smtp_user = username@

    smtp_port = 25

    smtp_password = userpasswd

    smtp_mail_from = username@

  • 多用户登录设置 (似乎只有CeleryExecutor支持)

    • 修改airflow.cfg中的下面3行配置

      authenticate = True

      auth_backend = airflow.contrib.auth.backends.password_auth

      filter_by_owner = True

    • 增加一个用户

      import airflow

      from airflow import models, settings

      from airflow.contrib.auth.backends.password_auth import PasswordUser

      user = PasswordUser(models.User())

      user.username = ‘ehbio’

      user.email = ‘mail@’

      user.password = ‘ehbio’

      session = settings.Session()

      session.add(user)

      mit()

      session.close()

      exit()

TASK

  • 参数解释
    • depends_on_past

      Airflow assumes idempotent tasks that operate on immutable data chunks. It also assumes that all task instance (each task for each schedule) needs to run.

      If your tasks need to be executed sequentially, you need to tell Airflow: use the depends_on_past=True flag on the tasks that require sequential execution.)

    • timestamp in format like 2016-01-01T00:03:00

    • Task中调用的命令出错后需要在网站Graph view中点击run手动重启。 为了方便任务修改后的顺利运行,有个折衷的方法是:

      • 设置 email_on_retry: True
      • 设置较长的retry_delay,方便在收到邮件后,立即做出处理
      • 然后再修改为较短的retry_delay,方便快速启动
  • 写完task DAG后,一定记得先检测下有无语法错误 python dag.py

  • 测试文件1:ct1.py
from airflow import DAG
from airflow.operators import BashOperator, MySqlOperator

from datetime import datetime, timedelta

one_min_ago = bine(datetime.today() -
	timedelta(minutes=1), datetime.min.time())

default_args = {
    'owner': 'airflow',         
		
	#为了测试方便,起始时间一般为当前时间减去schedule_interval
    'start_date': datatime(2016, 5, 29, 8, 30), 
    'email': ['chentong_biology@'],
    'email_on_failure': False, 
    'email_on_retry': False, 
	'depends_on_past': False, 
    'retries': 1, 
    'retry_delay': timedelta(minutes=5), 
    #'queue': 'bash_queue',
    #'pool': 'backfill', 
    #'priority_weight': 10, 
	#'end_date': datetime(2016, 5, 29, 11, 30), 
}

# DAG id 'ct1'必须是unique的, 一般与文件名相同
dag = DAG('ct1', default_args=default_args,
    schedule_interval="@once")

t1 = BashOperator(
    task_id='print_date', 
    bash_command='date', 
    dag=dag)

#cmd = "/home/test/test.bash " 注意末尾的空格
t2 = BashOperator(
    task_id='echo', 
    bash_command='echo "test" ', 
    retries=3, 
    dag=dag)

templated_command = """
    
"""
t3 = BashOperator(
    task_id='templated', 
    bash_command=templated_command, 
    params={'my_param': "Parameter I passed in"}, 
    dag=dag)

# This means that t2 will depend on t1 running successfully to run
# It is equivalent to t1.set_downstream(t2)
t2.set_upstream(t1)


t3.set_upstream(t1)

# all of this is equivalent to
# dag.set_dependency('print_date', 'sleep')
# dag.set_dependency('print_date', 'templated')
  • 测试文件2: ct2.py
from airflow import DAG
from airflow.operators import BashOperator

from datetime import datetime, timedelta

one_min_ago = bine(datetime.today() - timedelta(minutes=1),
                                  datetime.min.time())

default_args = {
    'owner': 'airflow',         
    'depends_on_past': True, 
    'start_date': one_min_ago,
    'email': ['chentong_biology@'],
    'email_on_failure': True, 
    'email_on_retry': True, 
    'retries': 5, 
    'retry_delay': timedelta(hours=30), 
    #'queue': 'bash_queue',
    #'pool': 'backfill', 
    #'priority_weight': 10, 
	#'end_date': datetime(2016, 5, 29, 11, 30), 
}

dag = DAG('ct2', default_args=default_args,
    schedule_interval="@once")

t1 = BashOperator(
    task_id='run1', 
    bash_command='(cd /home/ct/test; bash run1.sh -f ct_t1) ', 
    dag=dag)

t2 = BashOperator(
    task_id='run2', 
    bash_command='(cd /home/ct/test; bash run2.sh -f ct_t1) ', 
    dag=dag)

t2.set_upstream(t1)

  • run1.sh
#!/bin/bash

#set -x
set -e
set -u

usage()
{
cat <<EOF
${txtcyn}
Usage:

$0 options${txtrst}

${bldblu}Function${txtrst}:

This script is used to do ********************.

${txtbld}OPTIONS${txtrst}:
	-f	Data file ${bldred}[NECESSARY]${txtrst}
	-z	Is there a header[${bldred}Default TRUE${txtrst}]
EOF
}

file=
header='TRUE'

while getopts "hf:z:" OPTION
do
	case $OPTION in
		h)
			usage
			exit 1
			;;
		f)
			file=$OPTARG
			;;
		z)
			header=$OPTARG
			;;
		?)
			usage
			exit 1
			;;
	esac
done

if [ -z $file ]; then
	usage
	exit 1
fi

cat <<END >$file
A
B
C
D
E
F
G
END

sleep 20s
  • run2.sh
#!/bin/bash

#set -x
set -e
set -u

usage()
{
cat <<EOF
${txtcyn}
Usage:

$0 options${txtrst}

${bldblu}Function${txtrst}:

This script is used to do ********************.

${txtbld}OPTIONS${txtrst}:
	-f	Data file ${bldred}[NECESSARY]${txtrst}
EOF
}

file=
header='TRUE'

while getopts "hf:z:" OPTION
do
	case $OPTION in
		h)
			usage
			exit 1
			;;
		f)
			file=$OPTARG
			;;
		?)
			usage
			exit 1
			;;
	esac
done

if [ -z $file ]; then
	usage
	exit 1
fi

awk 'BEGIN{OFS=FS="\t"}{print $0, "53"}' $file >${file}.out

其它问题

  • The DagRun object has room for a conf parameter that gets exposed in the “context” (templates, operators, …). That is the place where you would associate parameters to a specific run. For now this is only possible in the context of an externally triggered DAG run. The way the TriggerDagRunOperator works, you can fill in the conf param during the execution of the callable that you pass to the operator.

    If you are looking to change the shape of your DAG through parameters, we recommend doing that using “singleton” DAGs (using a “@once” schedule_interval), meaning that you would write a Python program that generates multiple dag_ids, one of each run, probably based on metadata stored in a config file or elsewhere.

    The idea is that if you use parameters to alter the shape of your DAG, you break some of the assumptions around continuity of the schedule. Things like visualizing the tree view or how to perform a backfill becomes unclear and mushy. So if the shape of your DAG changes radically based on parameters, we consider those to be different DAGs, and you generate each one in your pipeline file.

  • 完全删掉某个DAG的信息

set @dag_id = 'BAD_DAG';
delete from airflow.xcom where dag_id = @dag_id;
delete from airflow.task_instance where dag_id = @dag_id;
delete from airflow.sla_miss where dag_id = @dag_id;
delete from airflow.log where dag_id = @dag_id;
delete from airflow.job where dag_id = @dag_id;
delete from airflow.dag_run where dag_id = @dag_id;
delete from airflow.dag where dag_id = @dag_id;
  • supervisord自动管理进程
[program:airflow_webserver]
command=/usr/local/bin/python2.7 /usr/local/bin/airflow webserver
user=airflow
environment=AIRFLOW_HOME="/home/airflow/airflow", PATH="/usr/local/bin:%(ENV_PATH)s"
stderr_logfile=/var/log/airflow-webserver.err.log
stdout_logfile=/var/log/airflow-webserver.out.log

[program:airflow_worker]
command=/usr/local/bin/python2.7 /usr/local/bin/airflow worker
user=airflow
environment=AIRFLOW_HOME="/home/airflow/airflow", PATH="/usr/local/bin:%(ENV_PATH)s"
stderr_logfile=/var/log/airflow-worker.err.log
stdout_logfile=/var/log/airflow-worker.out.log

[program:airflow_scheduler]
command=/usr/local/bin/python2.7 /usr/local/bin/airflow scheduler
user=airflow
environment=AIRFLOW_HOME="/home/airflow/airflow", PATH="/usr/local/bin:%(ENV_PATH)s"
stderr_logfile=/var/log/airflow-scheduler.err.log
stdout_logfile=/var/log/airflow-scheduler.out.log
  • 在特定情况下,修改DAG后,为了避免当前日期之前任务的运行,可以使用backfill填补特定时间段的任务
    • airflow backfill -s START -e END -mark_success DAG_ID

端口转发

  • 之前的配置都是在内网服务器进行的,但内网服务器只开放了SSH端口22,因此 我尝试在另外一台电脑上使用相同的配置,然后设置端口转发,把外网服务器 的rabbitmq的5672端口映射到内网服务器的对应端口,然后启动airflow连接 。

    • ssh -v -4 -NF -R 5672:127.0.0.1:5672 aliyun
    • 上一条命令表示的格式为

      ssh -R <local port>:<remote host>:<remote port> <SSH hostname>

      local port表示hostname的port

      Remote connections from LOCALHOST:5672 forwarded to local address 127.0.0.1:5672

    • -v: 在测试时打开
    • -4: 出现错误”bind: Cannot assign requested address”时,force the ssh client to use ipv4
    • 若出现”Warning: remote port forwarding failed for listen port 52698” ,关掉其它的ssh tunnel。

不同机器使用airflow

  • 在外网服务器(用做任务分发服务器)配置与内网服务器相同的airflow模块

  • 使用前述的端口转发以便外网服务器绕过内网服务器的防火墙访问rabbitmq 5672端口。

  • 在外网服务器启动 airflow webserver scheduler, 在内网服务器启动 airflow worker 发现任务执行状态丢失。继续学习Celery,以解决此问题。

安装redis (最后没用到)

  • http://download.redis.io/releases/redis-3.2.0.tar.gz
  • tar xvzf redis-3.2.0.tar.gz and make
  • redis-server启动redis
  • 使用ps -ef | grep 'redis'检测后台进程是否存在
  • 检测6379端口是否在监听netstat -lntp | grep 6379

任务未按预期运行可能的原因

  • 检查 start_date 和end_date是否在合适的时间范围内
  • 检查 airflow workerairflow scheduler和 airflow webserver --debug的输出,有没有某个任务运行异常
  • 检查airflow配置路径中logs文件夹下的日志输出
  • 若以上都没有问题,则考虑数据冲突,解决方式包括清空数据库或着给当前 dag一个新的dag_id

References

  1. https://github.com/airbnb/airflow
  2. http://www.csdn.net/article/1970-01-01/2825690
  3. http://www.cnblogs.com/harrychinese/p/airflow.html