RabbitMQ

一、 RabbitMQ安装及设置

• 服务端安装在Linux（Centos 6.5）上，客户端通过Python API来访问：

安装配置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
$ yum -y install rabbitmq-server

• 客户端（Windows）安装 python API：

pip install pika

• 添加用户并分配权限：

远程访问RabbitMQ在服务器上自己增加一个用户（用户为“xue”，密码为“xue123”），步骤如下：
（1） 创建一个name用户及为用户添加密码pass：sudo rabbitmqctl add_user name pass
（2） 设置该用户为administrator角色：sudo rabbitmqctl set_user_tags name administrator
（3） 设置权限：sudo rabbitmqctl set_permissions -p '/' name '.' '.' '.'
（4） 重启rabbitmq服务：service rabbitmq-server restart 或者 /etc/init.d/rabbitmq-server restart

• 查看队列数据：

rabbitmqctl list_queues

• 角色

management
policymaker
monitoring
administrator

• 配置文件

/etc/rabbitmq.config

• 日志路径

/var/log/rabbitmq

• 注意事项

注意要关闭防火墙(centos)：
1） 永久性生效，重启后不会复原
开启： chkconfig iptables on
关闭： chkconfig iptables off
2） 即时生效，重启后复原
开启： service iptables start
关闭： service iptables stop

二、 RabbitMQ基础知识

    AMQP，即Advanced Message Queuing Protocol，高级消息队列协议，是应用层协议的一个开放标准，为面向消息的中间件设计。消息中间件主要用于组件之间的解耦，消息的发送者无需知道消息使用者的存在，反之亦然。 AMQP的主要特征是面向消息、队列、路由（包括点对点和发布/订阅）、可靠性、安全。 RabbitMQ是一个由erlang开发的AMQP（Advanced Message Queuing Protocol）的开源实现。支持多种客户端，如：Python、Ruby、.NET、Java、JMS、C、PHP、ActionScript、XMPP、STOMP等，支持AJAX。用于在分布式系统中存储转发消息，在易用性、扩展性、高可用性等方面表现不俗。 下面将重点介绍RabbitMQ中的一些基础概念，了解了这些概念，是使用好RabbitMQ的基础。

 RabbitMQ的结构图如下：

说明：

    通过RabbitMQ的结构图能够清晰的看清楚整体的send Message到Receive Message的一个大致的流程。其中RabbitMQ Server部分可以看做是一个队列（Queue），工作过程就是 生产者（左边的Client A和Client B）产生数据 ----->  队列（Queue） ------> 消费者（右边的Client 1， Client 2和Client 3）处理数据 的一个过程，简化之后如下图所示。

• Message （消息）：RabbitMQ 转发的二进制对象，包括Headers（头）、Properties （属性）和 Data （数据），其中数据部分不是必要的。Producer（生产者）： 消息的生产者，负责产生消息并把消息发到交换机

• Exhange的应用

  • Consumer （消费者）：使用队列 Queue 从 Exchange 中获取消息的应用。
  • Exchange （交换机）：负责接收生产者的消息并把它转到到合适的队列

  • Queue （队列）：一个存储Exchange 发来的消息的缓冲，并将消息主动发送给Consumer，或者 Consumer 主动来获取消息。

  • Binding （绑定）：队列 和 交换机 之间的关系。Exchange 根据消息的属性和 Binding 的属性来转发消息。绑定的一个重要属性是 binding_key。

  • Connection （连接）和 Channel （通道）：生产者和消费者需要和 RabbitMQ 建立 TCP 连接。一些应用需要多个connection，为了节省TCP 连接，可以使用 Channel，它可以被认为是一种轻型的共享 TCP 连接的连接。连接需要用户认证，并且支持 TLS (SSL)。连接需要显式关闭。

补充说明：

    ConnectionFactory、Connection、Channel
    ConnectionFactory、Connection、Channel都是RabbitMQ对外提供的API中最基本的对象。
    Connection是RabbitMQ的socket链接，它封装了socket协议相关部分逻辑。
    ConnectionFactory为Connection的制造工厂。 Channel是我们与RabbitMQ打交道的最重要的一个接口，我们大部分的业务操作是在Channel这个接口中完成的，包括定义      Queue、定义Exchange、绑定Queue与Exchange、发布消息等。

三、操作RabbitMQ

1. 基于python的Queue实现生产者消费者模型：

#!/usr/bin/env python
# -*- coding:utf-8 -*-
import Queue
import threading
import time

message = Queue.Queue(10)

def producer(i):
    while True:
        time.sleep(1)
        print("producer --> %s" % i)
        message.put(i)

def consumer(i):
    while True:
        time.sleep(1)
        msg = message.get()
        print("consumer --> %s" % msg)

for i in range(15):
    t = threading.Thread(target=producer, args=(i,))
    t.start()

for i in range(10):
    t = threading.Thread(target=consumer, args=(i,))
    t.start()

2. 使用RabbitMQ实现的简单消息队列

Producer向queue发送消息，一个Consumer从该queue接收消息并打印

• 生产者 Producer

import pika


credentials = pika.PlainCredentials('xue', 'xue123')
connection = pika.BlockingConnection(pika.ConnectionParameters(
    '192.168.30.128',credentials=credentials))

channel = connection.channel() #建立了rabbit 协议的通道

# 声明queue
channel.queue_declare(queue='test')

# 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='test',
                      body='Hello World!')
print(" [x] Sent 'Hello World!'")
connection.close()

• 消费者 Consumer

import pika

credentials = pika.PlainCredentials('xue', 'xue123')
connection = pika.BlockingConnection(pika.ConnectionParameters(
    '192.168.30.128',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='test')


def callback(ch, method, properties, body):
    print("received msg...start processing....",body)
    print(" [x] msg process done....",body)


channel.basic_consume(callback,
                      queue='test',
                      no_ack=True)

print(' [*] Waiting for messages. To exit press CTRL+C')
channel.start_consuming()

• 执行结果

#Producer端：
[x] Sent 'Hello World!'

#Consumer端：
[*] Waiting for messages. To exit press CTRL+C
received msg...start processing.... b'Hello World!'
 [x] msg process done.... b'Hello World!'

3.  acknowledgment 消息确认不丢失

    1）数据丢失： 当设置no-ack ＝ True，默认为False。当消费者遇到情况(its channel is closed, connection is closed, or TCP connection is lost)挂掉了，那么数据就会丢失，因为rabbitmq默认会把此消息标记为已完成，然后从队列中移除。

    2）数据不丢失：当设置no-ack ＝ False，默认为False。那么当rabbitmq收到消费者的获取消息请求之后，标记为处理中，当再次收到ack之后，才会标记为已完成，然后从队列中将消息删除。当rabbitmq检测到消费者(its channel is closed, connection is closed, or TCP connection is lost)挂掉了，还没收到ack，则会重新将消息放回消息队列，交给下一个消费者处理，保证消息不丢失，也就是说，RabbitMQ给了消费者足够长的时间来做数据处理。

• 设置no-ack ＝ True

生产者代码不变，消费者代码如下：

import pika

credentials = pika.PlainCredentials('xue', 'xue123')
connection = pika.BlockingConnection(pika.ConnectionParameters(
    '192.168.30.128',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='test')

def callback(ch, method, properties, body):
    print("received msg...start processing.... ", body)
    print(" [x] msg process done.... ", body)

channel.basic_consume(callback,
                      queue='test',
                      no_ack=True)  # 不进行应答

print(' [*] Waiting for messages. To exit press CTRL+C')
channel.start_consuming()

• 执行结果（生产者发消息，消费者1和消费者2收消息，当断开消费者1，此时消费者2没有收到消息）

#Producer端：
[x] Sent 'Hello World!'

#Consumer端1：
[*] Waiting for messages. To exit press CTRL+C
received msg...start processing.... b'Hello World!'
 [x] msg process done.... b'Hello World!'

#当断开Consumer端1
#Consumer端2：
[*] Waiting for messages. To exit press CTRL+C

• 设置no-ack ＝ False

生产者代码不变，消费者代码如下：

import pika

credentials = pika.PlainCredentials('xue', 'xue123')
connection = pika.BlockingConnection(pika.ConnectionParameters(
    '192.168.30.128',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='test')

def callback(ch, method, properties, body):
    print("received msg...start processing.... ", body)
    print(" [x] msg process done.... ", body)

channel.basic_consume(callback,
                      queue='test',
                      no_ack=False)

print(' [*] Waiting for messages. To exit press CTRL+C')
channel.start_consuming()

• 执行结果（生产者发消息，消费者1和消费者2收消息，当断开消费者1，此时消费者2收到消息）

#Producer端：
[x] Sent 'Hello World!'

#Consumer端1：
[*] Waiting for messages. To exit press CTRL+C
received msg...start processing.... b'Hello World!'
 [x] msg process done.... b'Hello World!'

#当断开Consumer端1
#Consumer端2：
[*] Waiting for messages. To exit press CTRL+C
received msg...start processing.... b'Hello World!'
 [x] msg process done.... b'Hello World!'

• 设置no-ack ＝ False 并应答

import pika

credentials = pika.PlainCredentials('xue', 'xue123')
connection = pika.BlockingConnection(pika.ConnectionParameters(
    '192.168.30.128',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='test')

def callback(ch, method, properties, body):
    print("received msg...start processing.... ", body)
    print(" [x] msg process done.... ", body)
    ch.basic_ack(delivery_tag=method.delivery_tag)  # 发送ack消息

channel.basic_consume(callback,
                      queue='test',
                      no_ack=False) 

print(' [*] Waiting for messages. To exit press CTRL+C')
channel.start_consuming()

• 执行结果（生产者发消息，消费者1和消费者2收消息，当断开消费者1，此时消费者2未收到消息，此时已应答）

#Producer端：
[x] Sent 'Hello World!'

#Consumer端1：
[*] Waiting for messages. To exit press CTRL+C
received msg...start processing.... b'Hello World!'
 [x] msg process done.... b'Hello World!'

#当断开Consumer端1
#Consumer端2：
[*] Waiting for messages. To exit press CTRL+C

4. durable   消息不丢失

消息确认机制使得消费者端在崩溃的时候，RabbitMQ服务端消息不丢失，但是如果rabbitmq奔溃了呢？该如何保证队列中的消息不丢失？ 此就需要product在往队列中push消息的时候，告诉rabbitmq，此队列中的消息需要持久化，用到的参数：durable=True。通过设置 durable=True 参数可以当RabbitMQ重启等操作时，生产者端的消息队列数据依然存在。（有人发现消费者端也需要设置 channel.queue_declare(queue='task_queue',durable=True)，但是我在测试过程中去掉也是可以正常运行。）

• 生产者

import pika
import time
import sys

credentials = pika.PlainCredentials('xue', 'xue123')
connection = pika.BlockingConnection(pika.ConnectionParameters(
    '192.168.30.128',credentials=credentials))

channel = connection.channel()
# 声明queue make message persistent
channel.queue_declare(queue='task_queue',durable=True)

message = ' '.join(sys.argv[1:]) or "Hello World! %s" % time.time()

# 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='task_queue',
                      body=message,
                      properties=pika.BasicProperties(
                      delivery_mode=2,  # make message persistent
                      )
                )
print(" [x] Sent ", message)
connection.close()

• 消费者

import pika
import time

credentials = pika.PlainCredentials('xue', 'xue123')
connection = pika.BlockingConnection(pika.ConnectionParameters(
    '192.168.30.128',credentials=credentials))

channel = connection.channel()
# make message persistent
channel.queue_declare(queue='task_queue',durable=True)

def callback(ch, method, properties, body):
    print(" [x] Received ", body)
    time.sleep(2)
    print(" [x] Done")
    ch.basic_ack(delivery_tag=method.delivery_tag)

channel.basic_consume(callback,
                      queue='task_queue',
                      no_ack=False
                      )

print(' [*] Waiting for messages. To exit press CTRL+C')
channel.start_consuming()

• 消息队列查看结果

#未运行生产者端查看消息队列
[root@centos ~]# rabbitmqctl list_queues
Listing queues ...
task_queue      0
...done.
#运行生产者端并查看消息队列 task_queue 存在
[root@centos ~]# rabbitmqctl list_queues
Listing queues ...
task_queue      1
...done.
#重启 rabbitmq服务
[root@centos ~]# service rabbitmq-server restart
Restarting rabbitmq-server: SUCCESS
rabbitmq-server.
#重启 rabbitmq 服务查看消息队列 task_queue 依然存在
[root@centos ~]# rabbitmqctl list_queues
Listing queues ...
task_queue      1
...done.
[root@centos ~]#

• 运行结果

#运行生产者端
[x] Sent  Hello World! 1547381295.894814

#重启RabbitMQ后 运行消费者端
[*] Waiting for messages. To exit press CTRL+C
 [x] Received  b'Hello World! 1547381576.0062277'
 [x] Done

5. 消息获取顺序

默认消息队列里的数据是按照顺序被消费者拿走，例如：消费者1 去队列中获取 奇数 序列的任务，消费者2去队列中获取 偶数 序列的任务。

通过 channel.basic_qos(prefetch_count=1) 表示谁来谁取，不再按照奇偶数排列。设置prefetchCount=1，则Queue每次给每个消费者发送一条消息；消费者处理完这条消息后Queue会再给该消费者发送一条消息。

• 未加 channel.basic_qos(prefetch_count=1) 
• 生产者

import pika

credentials = pika.PlainCredentials('xue', 'xue123')
connection = pika.BlockingConnection(pika.ConnectionParameters(
    '192.168.30.128',credentials=credentials))

channel = connection.channel()
# 声明queue make message persistent
channel.queue_declare(queue='task_queue',durable=True)

# n RabbitMQ a message can never be sent directly to the queue, it always needs to go through an exchange.
for i in range(10):
    channel.basic_publish(exchange='',
                          routing_key='task_queue',
                          body="send %s" % str(i),
                          properties=pika.BasicProperties(
                          delivery_mode=2,  # make message persistent
                          )
                    )
    print(" [x] Sent ", str(i))
connection.close()

• 消费者

import pika
import time

credentials = pika.PlainCredentials('xue', 'xue123')
connection = pika.BlockingConnection(pika.ConnectionParameters(
    '192.168.30.128',credentials=credentials))

channel = connection.channel()
# make message persistent
#channel.queue_declare(queue='task_queue',durable=True)

def callback(ch, method, properties, body):
    print(" [x] Received ", body)
    time.sleep(2)
    print(" [x] Done")
    ch.basic_ack(delivery_tag=method.delivery_tag)

# channel.basic_qos(prefetch_count=1)

channel.basic_consume(callback,
                      queue='task_queue',
                      no_ack=False
                      )

print(' [*] Waiting for messages. To exit press CTRL+C')
channel.start_consuming()

• 执行结果

#运行一个生产者，两个消费者（先运行两个消费者，然后运行生产者）
#生产者端
[x] Sent  0
 [x] Sent  1
 [x] Sent  2
 [x] Sent  3
 [x] Sent  4
 [x] Sent  5
 [x] Sent  6
 [x] Sent  7
 [x] Sent  8
 [x] Sent  9

#消费者端1
 [*] Waiting for messages. To exit press CTRL+C
 [x] Received  b'send 1'
 [x] Done
 [x] Received  b'send 3'
 [x] Done
 [x] Received  b'send 5'
 [x] Done
 [x] Received  b'send 7'
 [x] Done
 [x] Received  b'send 9'
 [x] Done

#消费者端2
[*] Waiting for messages. To exit press CTRL+C
 [x] Received  b'send 0'
 [x] Done
 [x] Received  b'send 2'
 [x] Done
 [x] Received  b'send 4'
 [x] Done
 [x] Received  b'send 6'
 [x] Done
 [x] Received  b'send 8'
 [x] Done

• 加 channel.basic_qos(prefetch_count=1)  
• 消费者端1

import pika

credentials = pika.PlainCredentials('xue', 'xue123')
connection = pika.BlockingConnection(pika.ConnectionParameters(
    '192.168.30.128',credentials=credentials))

channel = connection.channel()
# make message persistent
#channel.queue_declare(queue='task_queue',durable=True)

def callback(ch, method, properties, body):
    print(" [x] Received ", body)
    print(" [x] Done")
    ch.basic_ack(delivery_tag=method.delivery_tag)

channel.basic_qos(prefetch_count=1)

channel.basic_consume(callback,
                      queue='task_queue',
                      no_ack=False
                      )

print(' [*] Waiting for messages. To exit press CTRL+C')
channel.start_consuming()

• 消费者端2（sleep(2)）

import pika
import time

credentials = pika.PlainCredentials('xue', 'xue123')
connection = pika.BlockingConnection(pika.ConnectionParameters(
    '192.168.30.128',credentials=credentials))

channel = connection.channel()
# make message persistent
#channel.queue_declare(queue='task_queue',durable=True)

def callback(ch, method, properties, body):
    print(" [x] Received ", body)
    time.sleep(2)
    print(" [x] Done")
    ch.basic_ack(delivery_tag=method.delivery_tag)

channel.basic_qos(prefetch_count=1)

channel.basic_consume(callback,
                      queue='task_queue',
                      no_ack=False
                      )

print(' [*] Waiting for messages. To exit press CTRL+C')
channel.start_consuming()

• 执行结果

#先运行消费者端1和消费者端2，再运行生产者端
#生产者端
 [x] Sent  0
 [x] Sent  1
 [x] Sent  2
 [x] Sent  3
 [x] Sent  4
 [x] Sent  5
 [x] Sent  6
 [x] Sent  7
 [x] Sent  8
 [x] Sent  9

#消费者端1
 [*] Waiting for messages. To exit press CTRL+C
 [x] Received  b'send 1'
 [x] Done
 [x] Received  b'send 2'
 [x] Done
 [x] Received  b'send 3'
 [x] Done
 [x] Received  b'send 4'
 [x] Done
 [x] Received  b'send 5'
 [x] Done
 [x] Received  b'send 6'
 [x] Done
 [x] Received  b'send 7'
 [x] Done
 [x] Received  b'send 8'
 [x] Done
 [x] Received  b'send 9'
 [x] Done


#消费者端2
 [*] Waiting for messages. To exit press CTRL+C
 [x] Received  b'send 0'
 [x] Done

 6. 使用Exchanges

    上面讨论的生产者将消息直接投递到Queue中，实际上这在RabbitMQ中这种事情永远都不会发生。实际的情况是，生产者将消息发送到Exchange（交换器，下图中的X），由Exchange将消息路由到一个或多个Queue中（或者丢弃）。 

     RabbitMQ中的Exchange有四种类型（fanout、direct、topic、headers），不同的类型有着不同的路由策略。

（1）首先介绍 routing key， Binding，Binding key作用及之间的关系。

• routing key

    生产者在将消息发送给Exchange的时候，一般会指定一个routing key，来指定这个消息的路由规则，而这个routing key需要与Exchange Type及binding key联合使用才能最终生效。 在Exchange Type与binding key固定的情况下（在正常使用时一般这些内容都是固定配置好的），我们的生产者就可以在发送消息给Exchange时，通过指定routing key来决定消息流向哪里。 RabbitMQ为routing key设定的长度限制为255 bytes。

• Binding

    RabbitMQ中通过Binding将Exchange与Queue关联起来，这样RabbitMQ就知道如何正确地将消息路由到指定的Queue了。

• Binding key

    在绑定（Binding）Exchange与Queue的同时，一般会指定一个binding key；消费者将消息发送给Exchange时，一般会指定一个routing key；当binding key与routing key相匹配时，消息将会被路由到对应的Queue中。这个将在Exchange Types章节会列举实际的例子加以说明。 在绑定多个Queue到同一个Exchange的时候，这些Binding允许使用相同的binding key。 binding key 并不是在所有情况下都生效，它依赖于Exchange Type，比如fanout类型的Exchange就会无视binding key，而是将消息路由到所有绑定到该Exchange的Queue。

（2）Exchange

    RabbitMQ常用的Exchange Type有fanout、direct、topic、headers这四种（AMQP规范里还提到两种Exchange Type，分别为system与自定义），下面分别进行介绍。

1）fanout（广播类型）

fanout类型的Exchange路由规则非常简单，它会把所有发送到该Exchange的消息路由到所有与它绑定的Queue中。 

上图中，生产者（P）发送到Exchange（X）的所有消息都会路由到图中的两个Queue，并最终被两个消费者（C1与C2）消费。

注意：由于fanout是广播类型，因此需要先启动消费者端，否则先启动生产者端再启动消费者端会接收不到数据（生产者已发送消息，但是生产者端错过消息接收）

• 生产者

import pika
import sys

credentials = pika.PlainCredentials('xue', 'xue123')
connection = pika.BlockingConnection(pika.ConnectionParameters(
    '192.168.30.128',credentials=credentials))
channel = connection.channel()

message = ' '.join(sys.argv[1:]) or "info: Hello World!"

channel.basic_publish(exchange='logs',
                      routing_key='',
                      body=message)
print(" [x] Sent ", message)
connection.close()

• 消费者

import pika

credentials = pika.PlainCredentials('xue', 'xue123')
connection = pika.BlockingConnection(pika.ConnectionParameters(
    '192.168.30.128',credentials=credentials))
channel = connection.channel()

channel.exchange_declare(exchange='logs', exchange_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] receive ", body)
    ch.basic_ack(delivery_tag=method.delivery_tag)

channel.basic_qos(prefetch_count=1)
channel.basic_consume(callback, queue=queue_name,no_ack=False)
channel.start_consuming()

• 执行结果

#生产者端（执行两次）
[x] Sent  info: Hello World!

#消费者端1
[*] Waiting for logs. To exit press CTRL+C
 [x] receive  b'info: Hello World!'
 [x] receive  b'info: Hello World!'

#消费者端2
[*] Waiting for logs. To exit press CTRL+C
 [x] receive  b'info: Hello World!'
 [x] receive  b'info: Hello World!'

2）direct（关键字类型）

direct类型的Exchange路由规则也很简单，它会把消息路由到那些binding key与routing key完全匹配的Queue中。 

以上图的配置为例，我们以routingKey=”error”发送消息到Exchange，则消息会路由到Queue1（amqp.gen-S9b…，这是由RabbitMQ自动生成的Queue名称）和Queue2（amqp.gen-Agl…）；如果我们以routingKey=”info”或routingKey=”warning”来发送消息，则消息只会路由到Queue2。如果我们以其他routingKey发送消息，则消息不会路由到这两个Queue中。

注意：先启动消费者端，否则先启动生产者端再启动消费者端会接收不到数据（生产者已发送消息，但是生产者端错过消息接收）

• 生产者

import pika
import sys
credentials = pika.PlainCredentials('xue', 'xue123')
connection = pika.BlockingConnection(pika.ConnectionParameters(
    '192.168.30.128',credentials=credentials))
channel = connection.channel()

channel.exchange_declare(exchange='direct_logs',exchange_type='direct')

severity = sys.argv[1] if len(sys.argv) > 1 else 'info' #严重程度,级别

message = ' '.join(sys.argv[2:]) or 'Hello World!'

channel.basic_publish(exchange='direct_logs',
                      routing_key=severity,
                      body=message)
print(" [x] Sent %r:%r" % (severity, message))
connection.close()

• 消费者

import pika
import sys

credentials = pika.PlainCredentials('xue', 'xue123')
connection = pika.BlockingConnection(pika.ConnectionParameters(
    '192.168.30.128',credentials=credentials))
channel = connection.channel()
channel.exchange_declare(exchange='direct_logs',exchange_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]
" % sys.argv[0])
    sys.exit(1)

for severity in severities:
    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] receive %r:%r" % (method.routing_key, body))
    ch.basic_ack(delivery_tag=method.delivery_tag)

channel.basic_qos(prefetch_count=1)
channel.basic_consume(callback,
                      queue=queue_name,
                      no_ack=False)

channel.start_consuming()

• 执行结果

3）topic（模糊匹配类型）

前面讲到direct类型的Exchange路由规则是完全匹配binding key与routing key，但这种严格的匹配方式在很多情况下不能满足实际业务需求。topic类型的Exchange在匹配规则上进行了扩展，它与direct类型的Exchage相似，也是将消息路由到binding key与routing key相匹配的Queue中，但这里的匹配规则有些不同，它约定：

• routing key为一个句点号“. ”分隔的字符串（我们将被句点号“. ”分隔开的每一段独立的字符串称为一个单词），如“stock.usd.nyse”、“nyse.vmw”、“quick.orange.rabbit”
• binding key与routing key一样也是句点号“. ”分隔的字符串
• binding key中可以存在两种特殊字符“*”与“#”，用于做模糊匹配，其中“*”用于匹配一个单词，“#”用于匹配多个单词（可以是零个）

以上图中的配置为例，routingKey=”quick.orange.rabbit”的消息会同时路由到Q1与Q2，routingKey=”lazy.orange.fox”的消息会路由到Q1与Q2，routingKey=”lazy.brown.fox”的消息会路由到Q2，routingKey=”lazy.pink.rabbit”的消息会路由到Q2（只会投递给Q2一次，虽然这个routingKey与Q2的两个bindingKey都匹配）；routingKey=”quick.brown.fox”、routingKey=”orange”、routingKey=”quick.orange.male.rabbit”的消息将会被丢弃，因为它们没有匹配任何bindingKey。

注意：先启动消费者端，否则先启动生产者端再启动消费者端会接收不到数据（生产者已发送消息，但是生产者端错过消息接收）

• 生产者

import pika
import sys

credentials = pika.PlainCredentials('xue', 'xue123')
connection = pika.BlockingConnection(pika.ConnectionParameters(
    '192.168.30.128',credentials=credentials))
channel = connection.channel()


channel.exchange_declare(exchange='topic_logs',exchange_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()

• 消费者

import pika
import sys

credentials = pika.PlainCredentials('xue', 'xue123')
connection = pika.BlockingConnection(pika.ConnectionParameters(
    '192.168.30.128',credentials=credentials))
channel = connection.channel()

channel.exchange_declare(exchange='topic_logs', exchange_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]...
" % 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] receive %r:%r" % (method.routing_key, body))
    ch.basic_ack(delivery_tag=method.delivery_tag)

channel.basic_qos(prefetch_count=1)
channel.basic_consume(callback,
                      queue=queue_name,
                      no_ack=False)

channel.start_consuming()

• 执行结果

4）headers

headers类型的Exchange不依赖于routing key与binding key的匹配规则来路由消息，而是根据发送的消息内容中的headers属性进行匹配。 在绑定Queue与Exchange时指定一组键值对；当消息发送到Exchange时，RabbitMQ会取到该消息的headers（也是一个键值对的形式），对比其中的键值对是否完全匹配Queue与Exchange绑定时指定的键值对；如果完全匹配则消息会路由到该Queue，否则不会路由到该Queue。 该类型的Exchange没有用到过（不过也应该很有用武之地），所以不做介绍。

RPC

MQ本身是基于异步的消息处理，前面的示例中所有的生产者（P）将消息发送到RabbitMQ后不会知道消费者（C）处理成功或者失败（甚至连有没有消费者来处理这条消息都不知道）。 但实际的应用场景中，我们很可能需要一些同步处理，需要同步等待服务端将我的消息处理完成后再进行下一步处理。这相当于RPC（Remote Procedure Call，远程过程调用）。在RabbitMQ中也支持RPC。

• 实现过程

• 客户端发送请求（消息）时，在消息的属性（MessageProperties ，在AMQP 协议中定义了properties ，这些属性会随着消息一起发送）中设置两个值replyTo （一个Queue 名称，用于告诉服务器处理完成后将通知我的消息发送到这个Queue 中）和correlationId （此次请求的标识号，服务器处理完成后需要将此属性返还，客户端将根据这个id了解哪条请求被成功执行了或执行失败）。

  self.channel.basic_publish(exchange='',
                             routing_key='rpc_queue',
                             properties=pika.BasicProperties(
                                 reply_to=self.callback_queue,
                                 correlation_id=self.corr_id,
                             ),
                             body=str(n))

• 服务器端收到消息并处理
• 服务器端处理完消息后，将生成一条应答消息到replyTo 指定的Queue ，同时带上correlationId 属性

  ch.basic_publish(exchange='',
                   routing_key=props.reply_to,
                   properties=pika.BasicProperties(correlation_id= props.correlation_id),
                   body=str(response))

• 客户端之前已订阅replyTo 指定的Queue ，从中收到服务器的应答消息后，根据其中的correlationId属性分析哪条请求被执行了，根据执行结果进行后续业务处理

计算斐波拉切数列例子：

• 生产者

import pika
import uuid

class FibonacciRpcClient(object):
    def __init__(self):
        credentials = pika.PlainCredentials('xue', 'xue123')
        self.connection = pika.BlockingConnection(pika.ConnectionParameters(
            '192.168.30.128', credentials=credentials))
        self.channel = self.connection.channel()

        #生成随机队列名称 reply_to=self.callback_queue
        result = self.channel.queue_declare(exclusive=True)
        self.callback_queue = result.method.queue

        self.channel.basic_consume(self.on_response, no_ack=False, #准备接受命令结果
                                   queue=self.callback_queue)

    def on_response(self, ch, method, props, body):
        """"callback方法"""
        if self.corr_id == props.correlation_id:
            self.response = body

    def call(self, n):
        self.response = None
        self.corr_id = str(uuid.uuid4()) #唯一标识符
        self.channel.basic_publish(exchange='',
                                   routing_key='rpc_queue',
                                   properties=pika.BasicProperties(
                                       reply_to=self.callback_queue,
                                       correlation_id=self.corr_id,
                                   ),
                                   body=str(n))

        count = 0
        while self.response is None:
            self.connection.process_data_events() #检查队列里有没有新消息,但不会阻塞
            count += 1
            print("check...",count)

        return int(self.response)

fibonacci_rpc = FibonacciRpcClient()

print(" [x] Requesting fib(30)")

response = fibonacci_rpc.call(10)
print(" [.] Got ", response)

• 消费者

import pika
import time

credentials = pika.PlainCredentials('xue', 'xue123')
connection = pika.BlockingConnection(pika.ConnectionParameters(
    '192.168.30.128',credentials=credentials))

channel = connection.channel()
channel.queue_declare(queue='rpc_queue')

def fib(n):
    if n == 0:
        return 0
    elif n == 1:
        return 1
    else:
        return fib(n - 1) + fib(n - 2)

def on_request(ch, method, props, body):
    n = int(body)

    print(" [.] fib(%s)" % n)
    response = fib(n)

    ch.basic_publish(exchange='',
                     routing_key=props.reply_to,
                     properties=pika.BasicProperties(correlation_id= props.correlation_id),
                     body=str(response))
    ch.basic_ack(delivery_tag=method.delivery_tag)


channel.basic_qos(prefetch_count=1)
channel.basic_consume(on_request, queue='rpc_queue')

print(" [x] Awaiting RPC requests")
channel.start_consuming()

• 计算结果

#消费者端
[x] Awaiting RPC requests
 [.] fib(10)

#生产者端
[x] Requesting fib(30)
check... 1
check... 2
check... 3
check... 4
check... 5
check... 6
check... 7
check... 8
check... 9
check... 10
 [.] Got  55

参考资料：

http://www.rabbitmq.com/getstarted.html（官网）

https://www.cnblogs.com/dwlsxj/p/RabbitMQ.html （RabbitMQ基础知识）

https://www.cnblogs.com/pycode/p/RabbitMQ.html 

http://www.cnblogs.com/wupeiqi/articles/5132791.html （武sir）

https://www.sojson.com/blog/48.html （为什么要选择RabbitMQ ，RabbitMQ简介，各种MQ选型对比）

https://www.cnblogs.com/enjoyall/p/7767462.html?utm_source=debugrun&utm_medium=referral