Airflow使用入门指南

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Airflow能做什么

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Airflow

是一个工作流分配管理系统,通过有向非循环图的方式管理任务流程,设置任务依赖关系和时间调度。

Airflow独立于我们要运行的任务,只需要把任务的名字和运行方式提供给Airflow作为一个task就可以。



安装和使用



最简单安装

在Linux终端运行如下命令 (需要已安装好

python2.x



pip

):

pip install airflow
pip install "airflow[crypto, password]"

安装成功之后,执行下面三步,就可以使用了。默认是使用的

SequentialExecutor

, 只能顺次执行任务。

  • 初始化数据库

    airflow initdb

    [必须的步骤]
  • 启动web服务器

    airflow webserver -p 8080

    [方便可视化管理dag]
  • 启动任务

    airflow scheduler

    [scheduler启动后,DAG目录下的dags就会根据设定的时间定时启动]
  • 此外我们还可以直接测试单个DAG,如测试文章末尾的DAG

    airflow test ct1 print_date 2016-05-14

最新版本的Airflow可从

https://github.com/apache/incubator-airflow

下载获得,解压缩按照安装python包的方式安装。



配置

mysql

以启用

LocalExecutor



CeleryExecutor

  • 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

    ct@server:~/airflow: mysql -uroot #以root身份登录mysql,默认无密码
    mysql> SET PASSWORD=PASSWORD("passwd");
    mysql> FLUSH PRIVILEGES; 
    # 注意sql语句末尾的分号
    
  • 新建用户和数据库

    # 新建名字为<airflow>的数据库
    mysql> CREATE DATABASE airflow; 
    # 新建用户`ct`,密码为`152108`, 该用户对数据库`airflow`有完全操作权限
        
    mysql> GRANT all privileges on airflow.* TO 'ct'@'localhost'  IDENTIFIED BY '152108'; 
    mysql> FLUSH PRIVILEGES; 
    
  • 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。

      ct@server:~/airflow: mysql -uct -p152108
      mysql> USE airflow;
      mysql> SHOW TABLES;
      +-------------------+
      | Tables_in_airflow |
      +-------------------+
      | alembic_version   |
      | chart             |
      | connection        |
      | dag               |
      | dag_pickle        |
      | dag_run           |
      | import_error      |
      | job               |
      | known_event       |
      | known_event_type  |
      | log               |
      | sla_miss          |
      | slot_pool         |
      | task_instance     |
      | users             |
      | variable          |
      | xcom              |
      +-------------------+
      17 rows in set (0.00 sec)
      
  • 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!
    

mariadb升级

curl -sS https://downloads.mariadb.com/MariaDB/mariadb_repo_setup | bash
cat <<EOF >/etc/yum.repos.d/MariaDB.repo
[mariadb]
name = MariaDB-10.3.14
baseurl=http://yum.mariadb.org/10.3.14/centos7-amd64
# alternative: baseurl=http://archive.mariadb.org/mariadb-10.3.14/yum/centos7-amd64
gpgkey=https://yum.mariadb.org/RPM-GPG-KEY-MariaDB
gpgcheck=1
EOF
rpm --import https://yum.mariadb.org/RPM-GPG-KEY-MariaDB
yum install MariaDB-server galera-4 MariaDB-client MariaDB-shared MariaDB-backup MariaDB-common



配置LocalExecutor

注:作为测试使用,此步可以跳过, 最后的生产环境用的是CeleryExecutor; 若CeleryExecutor配置不方便,也可使用LocalExecutor。

前面数据库已经配置好了,所以如果想使用LocalExecutor就只需要修改airflow配置文件就可以了。

airflow.cfg

文件通常在

~/airflow

目录下,打开更改

executor



executor = LocalExecutor

即完成了配置。

把文后

TASK

部分的dag文件拷贝几个到

~/airflow/dags

目录下,顺次执行下面的命令,然后打开网址

http://127.0.0.1:8080

就可以实时侦测任务动态了:

ct@server:~/airflow: airflow initdb` (若前面执行过,就跳过)
ct@server:~/airflow: airflow webserver --debug &
ct@server:~/airflow: airflow scheduler



配置CeleryExecutor (rabbitmq支持)

  • 安装airflow的celery和rabbitmq组件

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

    • 如果能直接使用

      yum



      apt-get

      安装则万事大吉。

    • 我使用的CentOS6则不能,需要如下一番折腾,

      # (Centos6,[REF](http://www.rabbitmq.com/install-rpm.html))
      wget https://packages.erlang-solutions.com/erlang/esl-erlang/FLAVOUR_1_general/esl-erlang_18.3-1~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 http://www.rabbitmq.com/releases/rabbitmq-server/v3.6.1/rabbitmq-server-3.6.1-1.noarch.rpm
      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

      ,

      # 可以与broker_url相同
      celery_result_backend = amqp://ct:152108@localhost:5672/ct_airflow
      Format explanation: transport://userid:password@hostname:port/virtual_host
      
  • 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并刷新网页。
      • 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 其它配置

  • 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.163.com
    smtp_starttls = True
    smtp_ssl = False
    smtp_user = username@163.com
    smtp_port = 25
    smtp_password = userpasswd
    smtp_mail_from = username@163.com
    
  • 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(在airflow所在服务器的python下运行)

      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@ehbio.com'
      user.password = 'ehbio'
      session = settings.Session()
      session.add(user)
      session.commit()
      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.)

      如果在TASK本该运行却没有运行时,或者设置的

      interval



      @once

      时,推荐使用

      depends_on_past=False

      。我在运行dag时,有时会出现,明明上游任务已经运行结束,下游任务却没有启动,整个dag就卡住了。这时设置

      depends_on_past=False

      可以解决这类问题。


    • 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,能有时间做出处理
      • 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 = datetime.combine(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@163.com'],
        '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'必须在airflow中是unique的, 一般与文件名相同
    # 多个用户时可加用户名做标记
    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 = """
        {% for i in range(2) %}
            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)
    
    # 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 = datetime.combine(datetime.today() - timedelta(minutes=1),
                                      datetime.min.time())
    
    default_args = {
        'owner': 'airflow',         
        'depends_on_past': True, 
        'start_date': one_min_ago,
        'email': ['chentong_biology@163.com'],
        '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

  • 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

redis新版出现错误解决方案

  1. 升级GCC到9.0
yum -y install centos-release-scl
yum -y install devtoolset-9-gcc devtoolset-9-gcc-c++ devtoolset-9-binutils

# echo "source /opt/rh/devtoolset-9/enable" >>/etc/profile.d/custom.sh 
  1. server.h:54:10: fatal error: systemd/sd-daemon.h: No such file or directory
yum install -y  systemd-devel



任务未按预期运行可能的原因

  • 检查

    start_date



    end_date

    是否在合适的时间范围内
  • 检查

    airflow worker

    ,

    airflow scheduler




    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://pythonhosted.org/airflow/

  2. http://kintoki.farbox.com/post/ji-chu-zhi-shi/airflow

  3. http://www.jianshu.com/p/59d69981658a

  4. http://bytepawn.com/luigi-airflow-pinball.html

  5. https://github.com/airbnb/airflow

  6. https://media.readthedocs.org/pdf/airflow/latest/airflow.pdf

  7. http://www.csdn.net/article/1970-01-01/2825690

  8. http://www.cnblogs.com/harrychinese/p/airflow.html

  9. https://segmentfault.com/a/1190000005078547



声明

文章原写于http://blog.genesino.com/2016/05/airflow/。转载请注明出处。



版权声明:本文为qazplm12_3原创文章,遵循 CC 4.0 BY-SA 版权协议,转载请附上原文出处链接和本声明。