背景
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@example.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
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