使用Airflow实现简单的工作流调度-大数据培训

Airflow是一个以编程方式编写，安排和监视工作流的平台。

使用Airflow将工作流编写任务的有向无环图(DAG)。Aiflow计划程序在遵循指定的依赖项同时在一组工作线程上执行任务。丰富的命令实用程序使在DAG上执行复杂的调度变得轻而易举。丰富的用户界面使查看生产中正在运行的管道，监视进度以及需要时对问题进行故障排除变得容易。

一、编写Dag任务脚本

1. 启动阿里云服务器集群，并启动hadoop集群。

2. 配置集群节点间ssh免密登录。

[root@airflowairflow]# vim /etc/hosts
172.26.16.78airflow  airflow
172.26.16.41hadoop101 hadoop101
172.26.16.39hadoop102 hadoop102
172.26.16.40hadoop103 hadoop103
 
[root@airflow~]# ssh-keygen -t rsa
[root@airflow~]# ssh-copy-id hadoop101
[root@airflow~]# ssh-copy-id hadoop102
[root@airflow~]# ssh-copy-id hadoop103

3. 创建work-py目录用于存放python调度脚本，编写.py脚本

[root@airflow~]# mkdir -p /opt/module/work-py
[root@airflow~]# cd /opt/module/work-py/
[root@airflowwork-py]# vim test.py
 
#!/usr/bin/python
fromairflow import DAG
fromairflow.operators.bash_operator import BashOperator
fromdatetime import datetime, timedelta
 
default_args= {
    'owner': 'test_owner',
    'depends_on_past': True,
    'email': ['2473196869@qq.com'],
    'start_date':datetime(2020,12,15),
    '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('test', default_args=default_args, schedule_interval=timedelta(days=1))
 
t1 =BashOperator(
    task_id='dwd',
    bash_command='ssh hadoop103"spark-submit  --master yarn--deploy-mode client --driver-memory 1g --num-executors 2 --executor-cores 2--executor-memory 2g --class com.atguigu.member.controller.DwdMemberController--queue spark /root/work/com_atguigu_warehouse-1.0-SNAPSHOT-jar-with-dependencies.jar"',
    retries=3,
    dag=dag)
 
t2 =BashOperator(
    task_id='dws',
    bash_command='ssh hadoop103"spark-submit  --master yarn--deploy-mode client --driver-memory 1g --num-executors 2 --executor-cores 2--executor-memory 2g --class com.atguigu.member.controller.DwsMemberController--queue spark /root/work/com_atguigu_warehouse-1.0-SNAPSHOT-jar-with-dependencies.jar"',
    retries=3,
    dag=dag)
 
t3 =BashOperator(
    task_id='ads',
    bash_command='ssh hadoop103"spark-submit  --master yarn--deploy-mode client --driver-memory 1g --num-executors 2 --executor-cores 2--executor-memory 2g --class com.atguigu.member.controller.AdsMemberController--queue spark /root/work/com_atguigu_warehouse-1.0-SNAPSHOT-jar-with-dependencies.jar"',
    retries=3,
    dag=dag)
 
t2.set_upstream(t1)
t3.set_upstream(t2)

脚本解读：

default_args 设置默认参数

depends_on_past 是否开启任务依赖

schedule_interval 调度频率

retries 重试次数

start_date 开始时间

BashOperator 具体执行任务，如果为true前置任务必须成功完成才会走下一个依赖任务，如果为false则忽略是否成功完成。

task_id 任务唯一标识（必填）

bash_command 具体任务执行命令

set_upstream 设置依赖如上图所示ads任务依赖dws任务依赖dwd任务

注意：

必须导包

from airflow import DAG

from airflow.operators.bash_operator importBashOperator

4. 配置JDK

注意：ssh的目标机（hadoop002） /etc/bashrc里必须配置java环境变量，配置完毕后source。

(python3)[root@airflow work-py]# vim /etc/bashrc
(python3)[root@airflow work-py]# source /etc/bashrc

5. 查看Airflow配置文件，获取dag文件存放目录

(python3)[root@airflow work-py]# vim ~/airflow/airflow.cfg

6. 按照配置文件中配置的文件路径，创建dag文件存放目录，将.py脚本放入此目录。

(python3)[root@airflow work-py]# mkdir ~/airflow/dags
(python3)[root@airflow work-py]# cp test.py ~/airflow/dags/

7. 等待一段时间，刷新任务列表，可以看到列表中，已经出现test任务。

(python3)[root@airflow work-py]# airflow list_dags
-------------------------------------------------------------------
DAGS
-------------------------------------------------------------------
example_bash_operator
example_branch_dop_operator_v3
example_branch_operator
example_complex
example_external_task_marker_child
example_external_task_marker_parent
example_http_operator
example_kubernetes_executor_config
example_nested_branch_dag
example_passing_params_via_test_command
example_pig_operator
example_python_operator
example_short_circuit_operator
example_skip_dag
example_subdag_operator
example_subdag_operator.section-1
example_subdag_operator.section-2
example_trigger_controller_dag
example_trigger_target_dag
example_xcom
latest_only
latest_only_with_trigger
test
test_utils
tutorial

8. 刷新Airflow的web页面，已经出现test任务。

9. 点击运行test任务。

10. 点击成功任务，查看日志。

11. 查看dag图，甘特图。

12. 查看脚本代码。

Dag任务操作

1. 删除dag任务。

2. 通过执行以下命令，可以重新添加dag任务。

(python3)[root@airflow work-py]# airflow list_tasks  test --tree
The'list_tasks' command is deprecated and removed in Airflow 2.0, please use'tasks list' instead
[2020-12-1511:17:08,981] {__init__.py:50} INFO - Using executor SequentialExecutor
[2020-12-1511:17:08,982] {dagbag.py:417} INFO - Filling up the DagBag from/root/airflow/dags

3. 查看当前所有dag任务，可以看到test任务被重新添加了回来。

(python3)[root@airflow work-py]#
(python3)[root@airflow work-py]# airflow list_dags
The 'list_dags'command is deprecated and removed in Airflow 2.0, please use 'dags list', or'dags report' instead
[2020-12-1511:33:57,106] {__init__.py:50} INFO - Using executor SequentialExecutor
[2020-12-1511:33:57,106] {dagbag.py:417} INFO - Filling up the DagBag from/root/airflow/dags
 
-------------------------------------------------------------------
DAGS
-------------------------------------------------------------------
example_bash_operator
example_branch_dop_operator_v3
example_branch_operator
example_complex
example_external_task_marker_child
example_external_task_marker_parent
example_http_operator
example_kubernetes_executor_config
example_nested_branch_dag
example_passing_params_via_test_command
example_pig_operator
example_python_operator
example_short_circuit_operator
example_skip_dag
example_subdag_operator
example_subdag_operator.section-1
example_subdag_operator.section-2
example_trigger_controller_dag
example_trigger_target_dag
example_xcom
latest_only
latest_only_with_trigger
test
test_utils
tutorial

4. 重新添加的dag任务。

3配置邮件服务器

1. 首先确保所有邮箱已经开启SMTP服务。

2. 修改airflow配置文件，如下：

(python3)[root@airflow work-py]# vim ~/airflow/airflow.cfg
smtp_host= smtp.qq.com
smtp_starttls= True
smtp_ssl= False
smtp_user= 2473196869@qq.com
#smtp_user =
smtp_password= wjmfbxkfvypdebeg
#smtp_password =
smtp_port= 587
smtp_mail_from= 2473196869@qq.com

3. 重启Airflow。

(python3)[root@airflow airflow]# ps -ef|egrep 'scheduler|airflow-webserver'|grep -vgrep|awk '{print $2}'|xargs kill -15
(python3)[root@airflow airflow]# ps -ef |grep airflow
root       745    1  0 09:50 ?        00:00:00 /sbin/dhclient -1 -q -lf/var/lib/dhclient/dhclient--eth0.lease -pf /var/run/dhclient-eth0.pid -Hairflow eth0
root      7875 1851  0 12:51 pts/1    00:00:00 grep --color=auto airflow
(python3)[root@airflow airflow]# kill -15 745
 
 
(python3)[root@airflow airflow]# airflow webserver -p 8080 -D
(python3)[root@airflow airflow]# airflow scheduler -D

4. 重新编辑test.py脚本文件，并且替换。

[root@airflow~]# cd /opt/module/work-py/
[root@airflowwork-py]# vim test.py
 
#!/usr/bin/python
fromairflow import DAG
fromairflow.operators.bash_operator import BashOperator
fromairflow.operators.email_operator import EmailOperator
fromdatetime import datetime, timedelta
 
default_args= {
    'owner': 'test_owner',
    'depends_on_past': True,
    'email': ['2473196869@qq.com'],
    'start_date':datetime(2020,12,15),
    '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('test', default_args=default_args, schedule_interval=timedelta(days=1))
 
t1 =BashOperator(
    task_id='dwd',
    bash_command='ssh hadoop103"spark-submit  --master yarn--deploy-mode client --driver-memory 1g --num-executors 2 --executor-cores 2--executor-memory 2g --class com.atguigu.member.controller.DwdMemberController--queue spark /root/work/com_atguigu_warehouse-1.0-SNAPSHOT-jar-with-dependencies.jar"',
    retries=3,
    dag=dag)
 
t2 =BashOperator(
    task_id='dws',
    bash_command='ssh hadoop103"spark-submit  --master yarn--deploy-mode client --driver-memory 1g --num-executors 2 --executor-cores 2--executor-memory 2g --class com.atguigu.member.controller.DwsMemberController--queue spark /root/work/com_atguigu_warehouse-1.0-SNAPSHOT-jar-with-dependencies.jar"',
    retries=3,
    dag=dag)
 
t3 =BashOperator(
    task_id='ads',
    bash_command='ssh hadoop103"spark-submit  --master yarn--deploy-mode client --driver-memory 1g --num-executors 2 --executor-cores 2--executor-memory 2g --class com.atguigu.member.controller.AdsMemberController--queue spark /root/work/com_atguigu_warehouse-1.0-SNAPSHOT-jar-with-dependencies.jar"',
    retries=3,
dag=dag)
 
email=EmailOperator(
   task_id="email",
   to="2473196869@qq.com",
    subject="test-subject",
    html_content="test-content",
    cc="chaosong@qq.com",
   dag=dag)
 
t2.set_upstream(t1)
t3.set_upstream(t2)
email.set_upstream(t3)
 
(python3)[root@airflow work-py]# cp test.py ~/airflow/dags/

5. 查看页面是否生效。

6. 运行测试，查看运行情况和邮件。

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原文地址：https://blog.csdn.net/zjjcchina/article/details/126119361