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
from odps import ODPS
from odps import options
from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from datetime import datetime, timedelta
import time
#修改系统默认编码。
reload(sys)
sys.setdefaultencoding('utf8')
default_args = {
'owner': 'airflow',
'depends_on_past': False,
'start_date':datetime(2020,1,15),
'retry_delay': timedelta(minutes=5),
}
dag = DAG(
'Airiflow_MC', default_args=default_args, schedule_interval=timedelta(seconds=30))
#打印时间
def get_time():
print '当前时间是{}'.format(time.time())
return time.time()
#执行MaxCompute的查询任务
def mc_job ():
#MaxCompute参数设置
options.sql.settings = {'options.tunnel.limit_instance_tunnel': False, 'odps.sql.allow.fullscan': True}
odps = ODPS('**your-access-id**', '**your-secret-access-key**', '**your-default-project**',endpoint='**your-end-point**')
# project = odps.get_project('my_project') # 取到某个项目。
project = odps.get_project() # 取到默认项目。
# 获取表。
# t = odps.get_table('tableName')
# 接受传入的分区参数。
with odps.execute_sql('select * from tableName').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
