一、RabbitMQ
1、基础概念
rabbitMQ说白了就是一个消息队列，类似于Queue，也是生产者与消费者模型.只不过做了扩展,所不同的是Queue在内存中的消息队列，而RabbitMQ是部署在机器上的；
一般而言,生成者往队列中放数据，而消费者从队列中取数据;
import Queue import threading message = Queue.Queue(10) def producer(i): #生产者 while True: message.put(i) def consumer(i): #消费者 while True: msg = message.get(i) for i in range(5): t=threading.Thread(target=producer,args=(i,)) t.start() for i in range(2): t=threading.Thread(target=consumer,args=(i,)) t.start()
RabbitMQ单独安装在一台服务器上，作为一种消息中间件，生产者和消费者都需要能够连接上这台机器；
配置安装epel源: rpm -ivh http://dl.fedoraproject.org/pub/epel/6/i386/epel-release-6-8.noarch.rpm
安装erlang: yum -y install erlang
安装rabbitmq_server并启动服务: yum -y install rabbitmq-server /etc/init.d/rabbitmq-server start
客户端(生成者和消费者)需要安装API接口pika
#!/usr/bin/env python import pika connection = pika.BlockingConnection(pika.ConnectionParameters(host='192.168.72.80')) channel = connection.channel() #通道 channel.queue_declare(queue='hello') #生成队列，名为hello channel.basic_publish(exchange='', routing_key='hello', body='Hello World!') #往队列中插入的数据内容 print(" [x] Sent 'Hello World!'") connection.close()
#!/usr/bin/env python # _*_ coding:utf-8 _*_ import pika connection = pika.BlockingConnection(pika.ConnectionParameters( host='192.168.72.80')) channel = connection.channel() channel.queue_declare(queue='hello') #生成一个队列叫做hello,在消费者中可以省略，但前提是生成者首先运行起来，产生消息队列hello，否在在下面往hello队列中取数据的时候，会出错;队列不会重复去定义; def callback(ch, method, properties, body): #body表示拿到的数据 print(" [x] Received %r" % body) channel.basic_consume(callback, #从hello队列中拿数据，拿到数据就会执行callback函数；no_ack是为了防止消息丢失，如果为False，那么如果消费者没有给生成者返回ack，那么生成者就会将数据再次放入到队列中。 queue='hello', no_ack=True) print(' [*] Waiting for messages. To exit press CTRL+C') channel.start_consuming()
2、RabbitMQ消息失联策略
关于no_ack=False,如果消费者将队列中的数据取走而没有给应答,那么重新启动消费者的时候依然可以看到之前的数据。
#!/usr/bin/env python # _*_ coding:utf-8 _*_ import pika connection = pika.BlockingConnection(pika.ConnectionParameters(host='192.168.72.80')) channel = connection.channel() channel.queue_declare(queue='hello') def callback(ch, method, properties, body): print(" [x] Received %r" % body) import time time.sleep(10) #拿到数据之后，中断，再次执行看能否接受到数据 print 'ok' ch.basic_ack(delivery_tag = method.delivery_tag) channel.basic_consume(callback, queue='hello', no_ack=False) print(' [*] Waiting for messages. To exit press CTRL+C') channel.start_consuming()
3、RabbitMQ服务器异常策略
如果服务器宕机,列中的数据就会丢失，解决方法是利用持久化,即将数据保存早硬盘中;
持久化是在生产者部分实现的,要实现持久化，队列和消息必须都要持久化，对于消费者,也可以不指定，取决于先启动生产者还是消费者;
import pika connection = pika.BlockingConnection(pika.ConnectionParameters( host='192.168.72.80')) channel = connection.channel() # make message persistent channel.queue_declare(queue='hello', durable=True) channel.basic_publish(exchange='', routing_key='hello', body='Hello World!', properties=pika.BasicProperties( delivery_mode=2, # make message persistent )) print(" [x] Sent 'Hello World!'") connection.close()
import pika connection = pika.BlockingConnection(pika.ConnectionParameters(host='192.168.72.80')) channel = connection.channel() # make message persistent channel.queue_declare(queue='hello', durable=True) #对于之前创建的队列，不能够实现持久化；只能对新创建的队列实现持久化 def callback(ch, method, properties, body): print(" [x] Received %r" % body) import time time.sleep(10) print 'ok' ch.basic_ack(delivery_tag = method.delivery_tag) channel.basic_consume(callback, queue='hello', no_ack=False) print(' [*] Waiting for messages. To exit press CTRL+C') channel.start_consuming()
4、RabbitMQ消息获取顺序
假设有两个消费者，分别往队列中拿数据，一个拿奇数，一个拿偶数，奇数简单而偶数复杂，那么一个消费者永远繁忙，而另一个永远轻松，channel.basic_qos(prefetch_count=1) 表示谁来谁取，不再按照奇偶数排列；
5、RabbitMQ发布和订阅
在消息的生成者和消费者中间通过exchange消息中间件实现，发布者在消息发布到队列之前不会将数据发布到队列中，而是放置到exchange中(之前的exchange为空),exchange的名字可以是任意的，exchange可以和多个队列进行绑定，这样exchange会将数据向绑定的队列中各发送一份数据;这样就实现了订阅和发布的功能啦~~~
以下创建fanout
import pika import sys connection = pika.BlockingConnection(pika.ConnectionParameters( host='192.168.72.80')) channel = connection.channel() channel.exchange_declare(exchange='logs', #发布者只需绑定exchange type='fanout') message = ' '.join(sys.argv[1:]) or "info: Hello World!" channel.basic_publish(exchange='logs', routing_key='', body=message) print(" [x] Sent %r" % message) connection.close()
import pika connection = pika.BlockingConnection(pika.ConnectionParameters(host='192.168.72.80')) channel = connection.channel() channel.exchange_declare(exchange='logs', type='fanout') result = channel.queue_declare(exclusive=True) queue_name = result.method.queue channel.queue_bind(exchange='logs', #消费者需要将队列和exchange绑定起来 queue=queue_name) print(' [*] Waiting for logs. To exit press CTRL+C') def callback(ch, method, properties, body): print(" [x] %r" % body) channel.basic_consume(callback, queue=queue_name, no_ack=True) channel.start_consuming()
6、RabbitMQ实现发布订阅和关键字匹配
解释fanout类型:exchange收到消息会将数据直接发送给队列
关键字匹配:根据关键字将exchange的数据发送到不同的队列中;
#!/usr/bin/env python # _*_ coding:utf-8 _*_ import pika import sys connection = pika.BlockingConnection(pika.ConnectionParameters( host='192.168.72.80')) channel = connection.channel() channel.exchange_declare(exchange='direct_logs',type='direct') #必须为direct类型 message ='Hello World!' channel.basic_publish(exchange='direct_logs', routing_key='db', #生产者发送的关键字 body=message) print(" [x] Sent %r:%r" % ('dali', message)) connection.close()
#!/usr/bin/env python # _*_ coding:utf-8 _*_ import pika connection = pika.BlockingConnection(pika.ConnectionParameters(host='192.168.72.80')) channel = connection.channel() channel.exchange_declare(exchange='direct_logs', type='direct') result = channel.queue_declare(exclusive=True) queue_name = result.method.queue channel.queue_bind(exchange='direct_logs', queue=queue_name, routing_key="dali") channel.queue_bind(exchange='direct_logs', queue=queue_name, routing_key="db") #匹配到的关键字 print(' [*] Waiting for logs. To exit press CTRL+C') def callback(ch, method, properties, body): print(" [x] %r" % body) channel.basic_consume(callback,queue=queue_name,no_ack=True) channel.start_consuming()
#!/usr/bin/env python # _*_ coding:utf-8 _*_ import pika connection = pika.BlockingConnection(pika.ConnectionParameters(host='192.168.72.80')) channel = connection.channel() channel.exchange_declare(exchange='direct_logs', type='direct') result = channel.queue_declare(exclusive=True) queue_name = result.method.queue channel.queue_bind(exchange='direct_logs', queue=queue_name, routing_key="erbi") channel.queue_bind(exchange='direct_logs', queue=queue_name, routing_key="db") #匹配到的关键字 print(' [*] Waiting for logs. To exit press CTRL+C') def callback(ch, method, properties, body): print(" [x] %r" % body) channel.basic_consume(callback, queue=queue_name, no_ack=True) channel.start_consuming()
关键字模糊匹配:
#匹配0和或多个单词
*只能匹配一个单词
监控作业内容:
1、使用Redis实现配置信息和数据的传输与存储。 2、执行命令采集指标 3、机器分类、实现监控模板 如:{ 'CPU':{'cmd',}} 机器根据主机名取模板,key为主机名,value为模板； 课可以通过反射类实现: def cpu(): return 'CPU指标' s='hi' func=getattr(api,"hi") result = func() 其中api.py最少的监控指标为两项 4、某个指标超过多长时间内n次报警; 5、使用线程: a、每隔几秒，去取自己的模板 b、使用自己的模板 6、运行程序获取监控指标，通过Redis的订阅和发布功能实现;
二、Memcached
1、基础概念
将经常访问的内容放在缓存中，Memcache是一种缓存的机制，是管理内存的; Memcached不同于Redis，只有一种数据类型,即key-value；
安装memcached:
wget http://memcached.org/latest
tar -zxvf memcached-1.x.x.tar.gzcd memcached-1.x.x./configure && make && make test && sudo make installPS：依赖libevent yum install libevent-devel apt-get install libevent-dev使用python操作memcached需要安装相关模块:
https://pypi.python.org/pypi/python-memcached启动memcached: memcached -d -m 128 -u root -p 12000
成功启动之后，可以使用telnet连接memcached操作了.
2、set和set
import memcache mc = memcache.Client(['192.168.72.80:12000'], debug=True) #debug表示在执行的时候，如果出错，信息就会以debug的模式输出 mc.set("foo", "bar") ret = mc.get('foo') print ret
set还具有修改键值对的功能
3、Memcached集群
memcached集群主机中后面会追加权重值,在memcached server内存中会维护一个memcached主机列表，主机列表中主机的重复此处与权重值相关
假如要执行mc.set("foo", "bar") 1、将foo转换为一个数字 import binascii print memcache.cmemcache_hash('foo') #将字符串foo转换为数字 2、将该数字和主机列表长度求余数，将余数作为索引，以此来找到列表中主机的IP，将该KEY_VALUE值存入主机中;
4、add
增加键值对，如果已经存在该键，报错.
5、replace
修改某个键的值，如果该键不存在，报错/异常
6、set和set_multi
import memcache mc = memcache.Client([('192.168.72.80:12000',1)], debug=True) mc.set_multi({'key1': 'val1', 'key2': 'val2'}) print mc.get('key1')
7、delete和delete_multi
如果存在该键，删除，不存在，不出异常
import memcache mc = memcache.Client([('192.168.72.80:12000',1)], debug=True) mc.set_multi({'key1': 'val1', 'key2': 'val2'}) print mc.get('key1') mc.delete_multi(['key1','key2']) #键值存在，删除，不存在，报异常
8、get和get_multi
import memcache mc = memcache.Client([('192.168.72.80:12000',1)], debug=True) mc.set_multi({'key1': 'val1', 'key2': 'val2'}) print mc.get('key1') #mc.delete_multi(['key1','key2']) item_dict = mc.get_multi(["key1","key2"]) print item_dict #获取结果为字典
9、append和prepend
append:指定key的值，在该key后面追加内容
prepend：指定key的值，在该key的值前面追加内容
import memcache mc = memcache.Client([('192.168.72.80:12000',1)], debug=True) mc.set_multi({'k1': 'val1', 'key2': 'val2'}) print mc.get('k1') mc.append('k1','after') #如果K1的值之前不存在，报异常 print mc.get('k1') mc.prepend('k1','before') print mc.get('k1')
10、desr和incr
incr:自增,默认自增1
decr:自减，默认自减1
import memcache mc = memcache.Client(['192.168.72.80:12000'], debug=True) mc.set('k1','777') mc.incr('k1') print mc.get('k1') mc.incr('k1',10) mc.decr('k1') print mc.get('k1') mc.decr('k1',10) print mc.get('k1')
11、gets和cas
具体查看memcache.py源代码,本质上是利用socket来send和Recv来实现上述功能的;
redis文档:http://doc.redisfans.com/
三、SQLALchemy
参考地址:
http://www.cnblogs.com/alex3714/articles/5978329.html
sqlarchemy是一款著名的python ORM框架，可以让对数据库小白的用户或者开发者不必写蛋疼的SQL，而直接通过程序语言对数据库进行操作，sqlarchemy提供的程序语言方法，可以先转换为SQL，再通过SQL对数据库进行相关操作;
可以有四种方式+sqlarchemy进行连接数据库操作,我这里采用的是pymysql，因为windows上安装mysql-python之类的包，会出现各种问题，并且python3目前还不支持mysql-python；
1、创建表;
sqlarchemy创建数据库表有两种方式:MetaData和declarative_base
使用MetaData
from sqlalchemy import create_engine,select, Table, Column, Integer, String, MetaData, ForeignKey
metadata = MetaData()
user = Table('user', metadata,
Column('id', Integer, primary_key=True),
Column('name', String(20)),
)
color = Table('color', metadata,
Column('id', Integer, primary_key=True),
Column('name', String(20)),
)
engine = create_engine("mysql+pymysql://root:@192.168.74.20:3306/test", max_overflow=5)
metadata.create_all(engine)
declarative_base
from sqlalchemy import create_engine,and_,or_,func,Table
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Column, Integer, String,ForeignKey
from sqlalchemy.orm import sessionmaker,relationship
Base = declarative_base() #生成一个SqlORM 基类
Host2Group = Table('host_2_group',Base.metadata,
Column('host_id',ForeignKey('host.id'),primary_key=True),
Column('group_id',ForeignKey('group.id'),primary_key=True),
)
engine = create_engine("mysql+pymysql://root:@192.168.74.20:3306/test",echo=True) #echo=True表示打印出创建过程
class Host(Base):
__tablename__ = 'host'
id = Column(Integer,primary_key=True,autoincrement=True)
hostname = Column(String(64),unique=True,nullable=False)
ip_addr = Column(String(128),unique=True,nullable=False)
port = Column(Integer,default=22)
#group_id = Column(Integer, ForeignKey('group.id'))
groups = relationship('Group',
secondary=Host2Group,
backref='host_list')
#group =relationship("Group",backref='host_list')
#group =relationship("Group",back_populates='host_list')
def __repr__(self):
return "<id=%s,hostname=%s, ip_addr=%s>" %(self.id,
self.hostname,
self.ip_addr)
class Group(Base):
__tablename__ = 'group'
id = Column(Integer,primary_key=True)
name = Column(String(64),unique=True,nullable=False)
#host_id = Column(Integer,ForeignKey('hosts.id'))
#host_list =relationship("Host", back_populates="group")
#hosts =relationship("Host")
def __repr__(self):
return "<id=%s,name=%s>" %(self.id,self.name)
Base.metadata.create_all(engine) #创建所有表结构
2、增删改查
#!/usr/bin/env python
# -*- coding:utf-8 -*-
from sqlalchemy import create_engine, Table, Column, Integer, String, MetaData, ForeignKey
metadata = MetaData()
user = Table('user', metadata,
Column('id', Integer, primary_key=True),
Column('name', String(20)),
)
color = Table('color', metadata,
Column('id', Integer, primary_key=True),
Column('name', String(20)),
)
engine = create_engine("mysql+mysqldb://root:123@127.0.0.1:3306/s11", max_overflow=5)
conn = engine.connect()
# 创建SQL语句，INSERT INTO "user" (id, name) VALUES (:id, :name)
conn.execute(user.insert(),{'id':7,'name':'seven'})
conn.close()
# sql = user.insert().values(id=123, name='wu')
# conn.execute(sql)
# conn.close()
# sql = user.delete().where(user.c.id > 1)
# sql = user.update().values(fullname=user.c.name)
# sql = user.update().where(user.c.name == 'jack').values(name='ed')
# sql = select([user, ])
# sql = select([user.c.id, ])
# sql = select([user.c.name, color.c.name]).where(user.c.id==color.c.id)
# sql = select([user.c.name]).order_by(user.c.name)
# sql = select([user]).group_by(user.c.name)
# result = conn.execute(sql)
# print result.fetchall()
# conn.close()