send端

import pika

credentials = pika.PlainCredentials('zhou', '')
connection = pika.BlockingConnection(pika.ConnectionParameters(
host='10.0.1.14',credentials=credentials))
channel = connection.channel() #声明queue
channel.queue_declare(queue='hello') #n RabbitMQ a message can never be sent directly to the queue, it always needs to go through an exchange.
channel.basic_publish(exchange='',
routing_key='hello',
body='Hello World!')
print(" [x] Sent 'Hello World!'")
connection.close()

receive端

import pika

connection = pika.BlockingConnection(pika.ConnectionParameters(
'localhost'))
channel = connection.channel() #You may ask why we declare the queue again ‒ we have already declared it in our previous code.
# We could avoid that if we were sure that the queue already exists. For example if send.py program
#was run before. But we're not yet sure which program to run first. In such cases it's a good
# practice to repeat declaring the queue in both programs.
channel.queue_declare(queue='hello') def callback(ch, method, properties, body):
print(" [x] Received %r" % body) channel.basic_consume(callback,
queue='hello',
no_ack=True) print(' [*] Waiting for messages. To exit press CTRL+C')
channel.start_consuming()

消费一个消息可能会花好几秒。你可能会问,如果如果一个消费者启动了一个长的任务,消息发了一半然后死了。按我们现在的代码,一旦rabbitmq传递了一个消息到给消费者,然后rabbitmq就迅速的将这个消息从内存里删除。在这种情况下,如果你kill了一个消费者,我们就会失去了这个正在传递的消息。

但是我们不想失去这个消息。如果一个消费者死了,我想把这个任务传给另一个消费者。

为了确保一个消息从不丢失,rabbitmq支持消息确认。消费者处理完信息后会向rabbitmq发送一个消息确认,这样rabbitmq就可以从内存里删除这个消息了。

如果一个消费者死掉了,没有发ack,rabbitmq就会知道这个消息没有传递成功,就会把这个消息重新存到queue里。

如果此时有其它的消费者在线,rabbitmq就会迅速的将这个消息传递给其它的的消费者。这样就确保了消息没有丢失,即使这个消费者是偶尔挂了。

根本就没有超时时间一说,当那个消费者挂了,rabbitmq会重新转发那条消息。即使这个消息处理了很长时间都没关系。

rabbitmq消息确认默认是开启的(no_ack=Fault)。在之前的例子里我们显然用的no_ack=True。

队列及消息持久化

生产者

import pika

credentials = pika.PlainCredentials('zhou', '')
# connection = pika.BlockingConnection(pika.ConnectionParameters(host=url_1,
# credentials=credentials, ssl=ssl, port=port))
connection = pika.BlockingConnection(pika.ConnectionParameters(
host='10.0.1.14',credentials=credentials)) channel = connection.channel() # 声明queue,durable=True(队列持久化)
channel.queue_declare(queue='alex3',durable=True) # n RabbitMQ a message can never be sent directly to the queue, it always needs to go through an exchange.
channel.basic_publish(exchange='',
routing_key='alex3', #send msg to this queue
body='Hello World!23',
properties=pika.BasicProperties(
delivery_mode=2, # 消息持久化
)
) print(" [x] Sent 'Hello World!2'")
connection.close()

消费者

import pika
import time
credentials = pika.PlainCredentials('zhou', '')
connection = pika.BlockingConnection(pika.ConnectionParameters(
host='10.0.1.14',credentials=credentials)) channel = connection.channel()
# You may ask why we declare the queue again ‒ we have already declared it in our previous code.
# We could avoid that if we were sure that the queue already exists. For example if send.py program
# was run before. But we're not yet sure which program to run first. In such cases it's a good
# practice to repeat declaring the queue in both programs. channel.queue_declare(queue='alex3',durable=True)#队列持久化 def callback(ch, method, properties, body):
print(ch, method, properties) print(" [x] Received %r" % body)
time.sleep(1) channel.basic_consume(callback,
queue='alex3',
no_ack=True
)
channel.basic_qos(prefetch_count=1)
print(' [*] Waiting for messages. To exit press CTRL+C')
channel.start_consuming()

消息公平分发

如果Rabbit只管按顺序把消息发到各个消费者身上,不考虑消费者负载的话,很可能出现,一个机器配置不高的消费者那里堆积了很多消息处理不完,同时配置高的消费者却一直很轻松。为解决此问题,可以在各个消费者端,配置perfetch=1,意思就是告诉RabbitMQ在我这个消费者当前消息还没处理完的时候就不要再给我发新消息了。

channel.basic_qos(prefetch_count=1)

生产者

import pika,sys

credentials = pika.PlainCredentials('zhou', '')
# connection = pika.BlockingConnection(pika.ConnectionParameters(host=url_1,
# credentials=credentials, ssl=ssl, port=port))
connection = pika.BlockingConnection(pika.ConnectionParameters(
host='10.0.1.14',credentials=credentials)) channel = connection.channel() channel.queue_declare(queue='task_queue', durable=True) message = ' '.join(sys.argv[1:]) or "Hello World!"
channel.basic_publish(exchange='',
routing_key='task_queue',
body=message,
properties=pika.BasicProperties(
delivery_mode = 2, # make message persistent
))
print(" [x] Sent %r" % message)
connection.close()

消费者

import pika,time
credentials = pika.PlainCredentials('zhou', '')
# connection = pika.BlockingConnection(pika.ConnectionParameters(host=url_1,
# credentials=credentials, ssl=ssl, port=port))
connection = pika.BlockingConnection(pika.ConnectionParameters(
host='10.0.1.14',credentials=credentials))
channel = connection.channel()
channel.queue_declare(queue='task_queue', durable=True)
print(' [*] Waiting for messages. To exit press CTRL+C') def callback(ch, method, properties, body):
print(" [x] Received %r" % body)
time.sleep(body.count(b'.'))
print(" [x] Done")
ch.basic_ack(delivery_tag = method.delivery_tag) channel.basic_qos(prefetch_count=1)
channel.basic_consume(callback,
queue='task_queue') channel.start_consuming()

rabbitmq消息发布\订阅(Publish\Subscribe)

当要采用广播发消息的时候就要用到exchange了。

exchange是一个很简单的东西,一端从生产者里接收消息,另一端把消息推送到队列里。exchange必须清除的知道它从生产者里接收的消息要发送给谁。接收到的消息是应该被追加到指定的queue里?还是应该追加到很多个queue里?或者是扔掉?这个规则都是根据exchange的类型来定义的。

exchange的类型:

fanout: 所有bind到此exchange的queue都可以接收消息
direct: 通过routingKey和exchange决定的那个唯一的queue可以接收消息
topic:所有符合routingKey(此时可以是一个表达式)的routingKey所bind的queue可以接收消息

   表达式符号说明:#代表一个或多个字符,*代表任何字符
      例:#.a会匹配a.a,aa.a,aaa.a等
          *.a会匹配a.a,b.a,c.a等
     注:使用RoutingKey为#,Exchange Type为topic的时候相当于使用fanout 

headers: 通过headers 来决定把消息发给哪些queue

fanout实例

send端

import pika
import sys credentials = pika.PlainCredentials('zhou', '')
connection = pika.BlockingConnection(pika.ConnectionParameters(
host='10.0.1.14',credentials=credentials)) channel = connection.channel() channel.exchange_declare(exchange='logs', 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()

recv端

import pika

credentials = pika.PlainCredentials('zhou', '')
connection = pika.BlockingConnection(pika.ConnectionParameters(
host='10.0.1.14',credentials=credentials)) channel = connection.channel() channel.exchange_declare(exchange='logs',type='fanout') result = channel.queue_declare(exclusive=True) # 不指定queue名字,rabbit会随机分配一个名字,exclusive=True会在使用此queue的消费者断开后,自动将queue删除 queue_name = result.method.queue channel.queue_bind(exchange='logs',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,
)
channel.start_consuming()

direct实例

send端

import pika
import sys credentials = pika.PlainCredentials('zhou', '')
connection = pika.BlockingConnection(pika.ConnectionParameters(
host='10.0.1.14',credentials=credentials)) channel = connection.channel() channel.exchange_declare(exchange='direct_logs',type='direct') severity = sys.argv[1] if len(sys.argv) > 1 else 'info' message = ' '.join(sys.argv[2:]) or 'Hello World!' channel.basic_publish(exchange='direct_logs',
routing_key=severity, #error
body=message) print(" [x] Sent %r:%r" % (severity, message))
connection.close()

recv端

import pika
import sys credentials = pika.PlainCredentials('zhou', '')
connection = pika.BlockingConnection(pika.ConnectionParameters(
host='10.0.1.14',credentials=credentials))
channel = connection.channel() channel.exchange_declare(exchange='direct_logs',type='direct')
result = channel.queue_declare(exclusive=True)
queue_name = result.method.queue severities = sys.argv[1:]
if not severities:
sys.stderr.write("Usage: %s [info] [warning] [error]\n" % sys.argv[0])
sys.exit(1) for severity in severities: #[error info warning]
channel.queue_bind(exchange='direct_logs',
queue=queue_name,
routing_key=severity)
print(' [*] Waiting for logs. To exit press CTRL+C')
def callback(ch, method, properties, body):
print(" [x] %r:%r" % (method.routing_key, body))
channel.basic_consume(callback,queue=queue_name,)
channel.start_consuming()

topic实例

send端

import pika
import sys credentials = pika.PlainCredentials('zhou', '')
connection = pika.BlockingConnection(pika.ConnectionParameters(
host='10.0.1.14',credentials=credentials)) channel = connection.channel() channel.exchange_declare(exchange='topic_logs',type='topic') routing_key = sys.argv[1] if len(sys.argv) > 1 else 'anonymous.info' message = ' '.join(sys.argv[2:]) or 'Hello World!' channel.basic_publish(exchange='topic_logs',
routing_key=routing_key,
body=message)
print(" [x] Sent %r:%r" % (routing_key, message))
connection.close()

recv端

import pika
import sys credentials = pika.PlainCredentials('zhou', '')
connection = pika.BlockingConnection(pika.ConnectionParameters(
host='10.0.1.14',credentials=credentials)) channel = connection.channel()
channel.exchange_declare(exchange='topic_logs',type='topic') result = channel.queue_declare(exclusive=True)
queue_name = result.method.queue binding_keys = sys.argv[1:]
if not binding_keys:
sys.stderr.write("Usage: %s [binding_key]...\n" % sys.argv[0])
sys.exit(1) for binding_key in binding_keys:
channel.queue_bind(exchange='topic_logs',
queue=queue_name,
routing_key=binding_key) print(' [*] Waiting for logs. To exit press CTRL+C')
def callback(ch, method, properties, body):
print(" [x] %r:%r" % (method.routing_key, body)) channel.basic_consume(callback,queue=queue_name) channel.start_consuming()

rabbitmq的更多相关文章

  1. 消息队列——RabbitMQ学习笔记

    消息队列--RabbitMQ学习笔记 1. 写在前面 昨天简单学习了一个消息队列项目--RabbitMQ,今天趁热打铁,将学到的东西记录下来. 学习的资料主要是官网给出的6个基本的消息发送/接收模型, ...

  2. RabbitMq应用二

    在应用一中,基本的消息队列使用已经完成了,在实际项目中,一定会出现各种各样的需求和问题,rabbitmq内置的很多强大机制和功能会帮助我们解决很多的问题,下面就一个一个的一起学习一下. 消息响应机制 ...

  3. 如何优雅的使用RabbitMQ

    RabbitMQ无疑是目前最流行的消息队列之一,对各种语言环境的支持也很丰富,作为一个.NET developer有必要学习和了解这一工具.消息队列的使用场景大概有3种: 1.系统集成,分布式系统的设 ...

  4. RabbitMq应用一的补充(RabbitMQ的应用场景)

    直接进入正题. 一.异步处理 场景:发送手机验证码,邮件 传统古老处理方式如下图 这个流程,全部在主线程完成,注册->入库->发送邮件->发送短信,由于都在主线程,所以要等待每一步完 ...

  5. RabbitMq应用一

    RabbitMq应用一 RabbitMQ的具体概念,百度百科一下,我这里说一下我的理解,如果有少或者不对的地方,欢迎纠正和补充. 一个项目架构,小的时候,一般都是传统的单一网站系统,或者项目,三层架构 ...

  6. 缓存、队列(Memcached、redis、RabbitMQ)

    本章内容: Memcached 简介.安装.使用 Python 操作 Memcached 天生支持集群 redis 简介.安装.使用.实例 Python 操作 Redis String.Hash.Li ...

  7. 消息队列性能对比——ActiveMQ、RabbitMQ与ZeroMQ(译文)

    Dissecting Message Queues 概述: 我花了一些时间解剖各种库执行分布式消息.在这个分析中,我看了几个不同的方面,包括API特性,易于部署和维护,以及性能质量..消息队列已经被分 ...

  8. windows下 安装 rabbitMQ 及操作常用命令

    rabbitMQ是一个在AMQP协议标准基础上完整的,可服用的企业消息系统.它遵循Mozilla Public License开源协议,采用 Erlang 实现的工业级的消息队列(MQ)服务器,Rab ...

  9. RabbitMQ + PHP (三)案例演示

    今天用一个简单的案例来实现 RabbitMQ + PHP 这个消息队列的运行机制. 主要分为两个部分: 第一:发送者(publisher) 第二:消费者(consumer) (一)生产者 (创建一个r ...

  10. RabbitMQ + PHP (二)AMQP拓展安装

    上篇说到了 RabbitMQ 的安装. 这次要在讲案例之前,需要安装PHP的AMQP扩展.不然可能会报以下两个错误. 1.Fatal error: Class 'AMQPConnection' not ...

随机推荐

  1. CoreOS和Docker入门

    转载自: http://www.oschina.net/translate/coreos_and_docker_first_steps?cmp CoreOS是一个基于Linux,systemd和Doc ...

  2. 关于FloatingActionButton

    由于FloatingActionButton本质上是ImageView,跟ImageView相关的就不介绍,这里重点介绍新加的几个属性. app:fabSize:FloatingActionButto ...

  3. 【hrbust2293】棋盘村

    题意 哈理工2016级新生程序设计全国邀请赛A题 http://acm.hrbust.edu.cn/index.php?m=ProblemSet&a=showProblem&probl ...

  4. jQuery文件上传插件Uploadify(转)

    一款基于flash的文件上传,有进度条和支持大文件上传,且可以多文件上传队列. 这款在flash的基础上增加了html5的支持,所以在移动端也可以使用. 由于官方提供的版本是flash免费,html5 ...

  5. iOS 开发总结(上)

    来源:蝴蝶之梦天使 链接:http://www.jianshu.com/p/d333cf6ae4b0 在iOS开发中经常需要使用的或不常用的知识点的总结,几年的收藏和积累(踩过的坑). 一. iPho ...

  6. python爬乌云dorps文章

    有时候翻看drops 无赖drops没有一个全部文章列表,所以就有了这个想法把所有文件标题链接都爬出来这样又直观又好找感兴趣的文章 #coding=utf-8 import re import url ...

  7. pecl 轻松安装php扩展

    PECL 的全称是 The PHP Extension Community Library ,是一个开放的并通过 PEAR(PHP Extension and Application Reposito ...

  8. Scrum meeting 记录

    本周Scrum Master 侯宇泰 一. 工作完成内容记录 · 陈双: 1. 后端数据库Azure Storage设计 2. Offline 上传Movie信息 3. 主页和配音页面的连接 · 鲍航 ...

  9. C#知识点记录

    用于记录C#知识要点. 参考:CLR via C#.C#并发编程.MSDN.百度 记录方式:读每本书,先看一遍,然后第二遍的时候,写笔记. CLR:公共语言运行时(Common Language Ru ...

  10. 网页访问全过程剖析[转].md

    本文转载自:http://www.cnblogs.com/wenanry/archive/2010/02/25/1673368.html 原文翻译自:http://igoro.com/archive/ ...