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airflow是Airbnb开源的一个用python编写的调度工具,基于有向无环图(DAG),airflow可以定义一组有依赖的任务,按照依赖依次执行,通过python代码定义子任务,并支持各种Operate操作器,灵活性大,能满足用户的各种需求。本文主要介绍使用Airflow的python Operator调度MaxCompute 任务
一、环境准备
Python 2.7.5 PyODPS支持Python2.6以上版本
Airflow apache-airflow-1.10.7
1.安装MaxCompute需要的包
pip install setuptools>=3.0
pip install requests>=2.4.0
pip install greenlet>=0.4.10 # 可选,安装后能加速Tunnel上传。
pip install cython>=0.19.0 # 可选,不建议Windows用户安装。
pip install pyodps
注意:如果requests包冲突,先卸载再安装对应的版本
2.执行如下命令检查安装是否成功
python -c "from odps import ODPS"
二、开发步骤
1.在Airflow家目录编写python调度脚本Airiflow_MC.py
- # -*- coding: UTF-8 -*-
- import sys
- import os
- from odps import ODPS
- from odps import options
- from airflow import DAG
- from airflow.operators.python_operator import PythonOperator
- from datetime import datetime, timedelta
- from configparser import ConfigParser
- import time
- reload(sys)
- sys.setdefaultencoding('utf8')
- #修改系统默认编码。
- # MaxCompute参数设置
- options.sql.settings = {'options.tunnel.limit_instance_tunnel': False, 'odps.sql.allow.fullscan': True}
- cfg = ConfigParser()
- cfg.read("odps.ini")
- print(cfg.items())
- odps = ODPS(cfg.get("odps","access_id"),cfg.get("odps","secret_access_key"),cfg.get("odps","project"),cfg.get("odps","endpoint"))
- default_args = {
- 'owner': 'airflow',
- 'depends_on_past': False,
- 'retry_delay': timedelta(minutes=5),
- 'start_date':datetime(2020,1,15)
- # 'email': ['airflow@cdxwcx.com'],
- # 'email_on_failure': False,
- # 'email_on_retry': False,
- # 'retries': 1,
- # 'queue': 'bash_queue',
- # 'pool': 'backfill',
- # 'priority_weight': 10,
- # 'end_date': datetime(2016, 1, 1),
- }
- dag = DAG(
- 'Airiflow_MC', default_args=default_args, schedule_interval=timedelta(seconds=30))
- def read_sql(sqlfile):
- with io.open(sqlfile, encoding='utf-8', mode='r') as f:
- sql=f.read()
- f.closed
- return sql
- def get_time():
- print '当前时间是{}'.format(time.time())
- return time.time()
- def mc_job ():
- project = odps.get_project() # 取到默认项目。
- instance=odps.run_sql("select * from long_chinese;")
- print(instance.get_logview_address())
- instance.wait_for_success()
- with instance.open_reader() as reader:
- count = reader.count
- print("查询表数据条数:{}".format(count))
- for record in reader:
- print record
- return count
- t1 = PythonOperator (
- task_id = 'get_time' ,
- provide_context = False ,
- python_callable = get_time,
- dag = dag )
- t2 = PythonOperator (
- task_id = 'mc_job' ,
- provide_context = False ,
- python_callable = mc_job ,
- dag = dag )
- t2.set_upstream(t1)
2.提交
- python Airiflow_MC.py
3.进行测试
- # print the list of active DAGs
- airflow list_dags
- # prints the list of tasks the "tutorial" dag_id
- airflow list_tasks Airiflow_MC
- # prints the hierarchy of tasks in the tutorial DAG
- airflow list_tasks Airiflow_MC --tree
- #测试task
- airflow test Airiflow_MC get_time 2010-01-16
- airflow test Airiflow_MC mc_job 2010-01-16
4.运行调度任务
登录到web界面点击按钮运行
5.查看任务运行结果
1.点击view log
2.查看结果
网站名称:使用AirFlow调度MaxCompute
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