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python/其他/08-Rabbitmq从入门到精通

2020-09-06

一 消息队列介绍

1.1 介绍

消息队列就是基础数据结构中的“先进先出”的一种数据机构。想一下,生活中买东西,需要排队,先排的人先买消费,就是典型的“先进先出”

image-20200907000210863

1.2 MQ解决什么问题

MQ是一直存在,不过随着微服务架构的流行,成了解决微服务之间问题的常用工具。

应用解耦

以电商应用为例,应用中有订单系统、库存系统、物流系统、支付系统。用户创建订单后,如果耦合调用库存系统、物流系统、支付系统,任何一个子系统出了故障,都会造成下单操作异常。

当转变成基于消息队列的方式后,系统间调用的问题会减少很多,比如物流系统因为发生故障,需要几分钟来修复。在这几分钟的时间里,物流系统要处理的内存被缓存在消息队列中,用户的下单操作可以正常完成。当物流系统恢复后,继续处理订单信息即可,中单用户感受不到物流系统的故障。提升系统的可用性

image-20200907000246573

流量消峰

举个栗子,如果订单系统最多能处理一万次订单,这个处理能力应付正常时段的下单时绰绰有余,正常时段我们下单一秒后就能返回结果。但是在高峰期,如果有两万次下单操作系统是处理不了的,只能限制订单超过一万后不允许用户下单。

使用消息队列做缓冲,我们可以取消这个限制,把一秒内下的订单分散成一段时间来处理,这事有些用户可能在下单十几秒后才能收到下单成功的操作,但是比不能下单的体验要好。

消息分发

多个服务队数据感兴趣,只需要监听同一类消息即可处理。

image-20200907000338060

例如A产生数据,B对数据感兴趣。如果没有消息的队列A每次处理完需要调用一下B服务。过了一段时间C对数据也感性,A就需要改代码,调用B服务,调用C服务。只要有服务需要,A服务都要改动代码。很不方便。

image-20200907000411649

有了消息队列后,A只管发送一次消息,B对消息感兴趣,只需要监听消息。C感兴趣,C也去监听消息。A服务作为基础服务完全不需要有改动

异步消息

image-20200907000500012

有些服务间调用是异步的,例如A调用B,B需要花费很长时间执行,但是A需要知道B什么时候可以执行完,以前一般有两种方式,A过一段时间去调用B的查询api查询。或者A提供一个callback api,B执行完之后调用api通知A服务。这两种方式都不是很优雅

image-20200907000523720

使用消息总线,可以很方便解决这个问题,A调用B服务后,只需要监听B处理完成的消息,当B处理完成后,会发送一条消息给MQ,MQ会将此消息转发给A服务。

这样A服务既不用循环调用B的查询api,也不用提供callback api。同样B服务也不用做这些操作。A服务还能及时的得到异步处理成功的消息

1.3 常见消息队列及比较

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结论:

Kafka在于分布式架构,RabbitMQ基于AMQP协议来实现,RocketMQ/思路来源于kafka,改成了主从结构,在事务性可靠性方面做了优化。广泛来说,电商、金融等对事务性要求很高的,可以考虑RabbitMQ和RocketMQ,对性能要求高的可考虑Kafka

二 Rabbitmq安装

官网:https://www.rabbitmq.com/getstarted.html

2.1 服务端原生安装

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# 安装配置epel源
# 安装erlang
yum -y install erlang
# 安装RabbitMQ
yum -y install rabbitmq-server

2.2 服务端Docker安装

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docker pull rabbitmq:management
docker run -di --name Myrabbitmq -e RABBITMQ_DEFAULT_USER=admin -e RABBITMQ_DEFAULT_PASS=admin -p 15672:15672 -p 5672:5672 rabbitmq:managemen

2.3 客户端安装

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pip3 install pika

2.4 设置用户和密码

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rabbitmqctl add_user lqz 123
# 设置用户为administrator角色
rabbitmqctl set_user_tags lqz administrator
# 设置权限
rabbitmqctl set_permissions -p "/" root ".*" ".*" ".*"

# 然后重启rabbiMQ服务
systemctl reatart rabbitmq-server

# 然后可以使用刚才的用户远程连接rabbitmq server了。

三 基于Queue实现生产者消费者模型

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import Queue
import threading

message = Queue.Queue(10)

def producer(i):
while True:
message.put(i)

def consumer(i):
while True:
msg = message.get()

for i in range(12):
t = threading.Thread(target=producer, args=(i,))
t.start()

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

四 基本使用(生产者消费者模型)

对于RabbitMQ来说,生产和消费不再针对内存里的一个Queue对象,而是某台服务器上的RabbitMQ Server实现的消息队列。

生产者

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import pika
# 无密码
# connection = pika.BlockingConnection(pika.ConnectionParameters('127.0.0.1'))

# 有密码
credentials = pika.PlainCredentials("admin","admin")
connection = pika.BlockingConnection(pika.ConnectionParameters('101.133.225.166',credentials=credentials))
channel = connection.channel()
# 声明一个队列(创建一个队列)
channel.queue_declare(queue='lqz')

channel.basic_publish(exchange='',
routing_key='lqz', # 消息队列名称
body='hello world')
connection.close()

消费者

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import pika

credentials = pika.PlainCredentials("admin","admin")
connection = pika.BlockingConnection(pika.ConnectionParameters('101.133.225.166',credentials=credentials))
channel = connection.channel()

# 声明一个队列(创建一个队列)
channel.queue_declare(queue='lqz')

def callback(ch, method, properties, body):
print("消费者接受到了任务: %r" % body)

channel.basic_consume(queue='lqz',on_message_callback=callback,auto_ack=True)

channel.start_consuming()

五 消息安全之ack

生产者

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import pika
# 无密码
# connection = pika.BlockingConnection(pika.ConnectionParameters('127.0.0.1'))

# 有密码
credentials = pika.PlainCredentials("admin","admin")
connection = pika.BlockingConnection(pika.ConnectionParameters('101.133.225.166',credentials=credentials))
channel = connection.channel()
# 声明一个队列(创建一个队列)
channel.queue_declare(queue='lqz')

channel.basic_publish(exchange='',
routing_key='lqz', # 消息队列名称
body='hello world')
connection.close()

消费者

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import pika

credentials = pika.PlainCredentials("admin","admin")
connection = pika.BlockingConnection(pika.ConnectionParameters('101.133.225.166',credentials=credentials))
channel = connection.channel()

# 声明一个队列(创建一个队列)
channel.queue_declare(queue='lqz')

def callback(ch, method, properties, body):
print("消费者接受到了任务: %r" % body)
# 通知服务端,消息取走了,如果auto_ack=False,不加下面,消息会一直存在
# ch.basic_ack(delivery_tag=method.delivery_tag)

channel.basic_consume(queue='lqz',on_message_callback=callback,auto_ack=False)

channel.start_consuming()

六 消息安全之durable持久化

生产者

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import pika
# 无密码
# connection = pika.BlockingConnection(pika.ConnectionParameters('127.0.0.1'))

# 有密码
credentials = pika.PlainCredentials("admin","admin")
connection = pika.BlockingConnection(pika.ConnectionParameters('101.133.225.166',credentials=credentials))
channel = connection.channel()
# 声明一个队列(创建一个队列),durable=True支持持久化,队列必须是新的才可以
channel.queue_declare(queue='lqz1',durable=True)

channel.basic_publish(exchange='',
routing_key='lqz1', # 消息队列名称
body='111',
properties=pika.BasicProperties(
delivery_mode=2, # make message persistent,消息也持久化
)
)
connection.close()

消费者

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import pika

credentials = pika.PlainCredentials("admin","admin")
connection = pika.BlockingConnection(pika.ConnectionParameters('101.133.225.166',credentials=credentials))
channel = connection.channel()

# 声明一个队列(创建一个队列)
channel.queue_declare(queue='lqz1')

def callback(ch, method, properties, body):
print("消费者接受到了任务: %r" % body)
# 通知服务端,消息取走了,如果auto_ack=False,不加下面,消息会一直存在
# ch.basic_ack(delivery_tag=method.delivery_tag)

channel.basic_consume(queue='lqz1',on_message_callback=callback,auto_ack=False)

channel.start_consuming()

七 闲置消费

正常情况如果有多个消费者,是按照顺序第一个消息给第一个消费者,第二个消息给第二个消费者

但是可能第一个消息的消费者处理消息很耗时,一直没结束,就可以让第二个消费者优先获得闲置的消息

生产者

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import pika
# 无密码
# connection = pika.BlockingConnection(pika.ConnectionParameters('127.0.0.1'))

# 有密码
credentials = pika.PlainCredentials("admin","admin")
connection = pika.BlockingConnection(pika.ConnectionParameters('101.133.225.166',credentials=credentials))
channel = connection.channel()
# 声明一个队列(创建一个队列),durable=True支持持久化,队列必须是新的才可以
channel.queue_declare(queue='lqz123',durable=True)

channel.basic_publish(exchange='',
routing_key='lqz123', # 消息队列名称
body='111',
properties=pika.BasicProperties(
delivery_mode=2, # make message persistent,消息也持久化
)
)
connection.close()

消费者

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import pika

credentials = pika.PlainCredentials("admin","admin")
connection = pika.BlockingConnection(pika.ConnectionParameters('101.133.225.166',credentials=credentials))
channel = connection.channel()

# 声明一个队列(创建一个队列)
# channel.queue_declare(queue='lqz123')

def callback(ch, method, properties, body):
print("消费者接受到了任务: %r" % body)
# 通知服务端,消息取走了,如果auto_ack=False,不加下面,消息会一直存在
ch.basic_ack(delivery_tag=method.delivery_tag)

channel.basic_qos(prefetch_count=1) #####就只有这一句话 谁闲置谁获取,没必要按照顺序一个一个来
channel.basic_consume(queue='lqz123',on_message_callback=callback,auto_ack=False)

channel.start_consuming()

八 发布订阅

发布者

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import pika
credentials = pika.PlainCredentials("admin","admin")
connection = pika.BlockingConnection(pika.ConnectionParameters('101.133.225.166',credentials=credentials))
channel = connection.channel()

channel.exchange_declare(exchange='m1',exchange_type='fanout')

channel.basic_publish(exchange='m1',
routing_key='',
body='lqz nb')

connection.close()

订阅者(启动几次订阅者会生成几个队列)

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import pika

credentials = pika.PlainCredentials("admin","admin")
connection = pika.BlockingConnection(pika.ConnectionParameters('101.133.225.166',credentials=credentials))
channel = connection.channel()

# exchange='m1',exchange(秘书)的名称
# exchange_type='fanout' , 秘书工作方式将消息发送给所有的队列
channel.exchange_declare(exchange='m1',exchange_type='fanout')

# 随机生成一个队列
result = channel.queue_declare(queue='',exclusive=True)
queue_name = result.method.queue
print(queue_name)
# 让exchange和queque进行绑定.
channel.queue_bind(exchange='m1',queue=queue_name)


def callback(ch, method, properties, body):
print("消费者接受到了任务: %r" % body)

channel.basic_consume(queue=queue_name,on_message_callback=callback,auto_ack=True)

channel.start_consuming()

九 发布订阅高级之Routing(按关键字匹配)

发布者

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import pika
credentials = pika.PlainCredentials("admin","admin")
connection = pika.BlockingConnection(pika.ConnectionParameters('101.133.225.166',credentials=credentials))
channel = connection.channel()

channel.exchange_declare(exchange='m2',exchange_type='direct')

channel.basic_publish(exchange='m2',
routing_key='bnb', # 多个关键字,指定routing_key
body='lqz nb')

connection.close()

订阅者1

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import pika

credentials = pika.PlainCredentials("admin","admin")
connection = pika.BlockingConnection(pika.ConnectionParameters('101.133.225.166',credentials=credentials))
channel = connection.channel()

# exchange='m1',exchange(秘书)的名称
# exchange_type='direct' , 秘书工作方式将消息发送给不同的关键字
channel.exchange_declare(exchange='m2',exchange_type='direct')

# 随机生成一个队列
result = channel.queue_declare(queue='',exclusive=True)
queue_name = result.method.queue
print(queue_name)
# 让exchange和queque进行绑定.
channel.queue_bind(exchange='m2',queue=queue_name,routing_key='nb')
channel.queue_bind(exchange='m2',queue=queue_name,routing_key='bnb')


def callback(ch, method, properties, body):
print("消费者接受到了任务: %r" % body)

channel.basic_consume(queue=queue_name,on_message_callback=callback,auto_ack=True)

channel.start_consuming()

订阅者2

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import pika

credentials = pika.PlainCredentials("admin","admin")
connection = pika.BlockingConnection(pika.ConnectionParameters('101.133.225.166',credentials=credentials))
channel = connection.channel()

# exchange='m1',exchange(秘书)的名称
# exchange_type='direct' , 秘书工作方式将消息发送给不同的关键字
channel.exchange_declare(exchange='m2',exchange_type='direct')

# 随机生成一个队列
result = channel.queue_declare(queue='',exclusive=True)
queue_name = result.method.queue
print(queue_name)
# 让exchange和queque进行绑定.
channel.queue_bind(exchange='m2',queue=queue_name,routing_key='nb')



def callback(ch, method, properties, body):
print("消费者接受到了任务: %r" % body)

channel.basic_consume(queue=queue_name,on_message_callback=callback,auto_ack=True)

channel.start_consuming()

九 发布订阅高级之Topic(按关键字模糊匹配)

发布者

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import pika
credentials = pika.PlainCredentials("admin","admin")
connection = pika.BlockingConnection(pika.ConnectionParameters('101.133.225.166',credentials=credentials))
channel = connection.channel()

channel.exchange_declare(exchange='m3',exchange_type='topic')

channel.basic_publish(exchange='m3',
# routing_key='lqz.handsome', #都能收到
routing_key='lqz.handsome.xx', #只有lqz.#能收到
body='lqz nb')

connection.close()

订阅者1

*只能加一个单词

#可以加任意单词字符

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import pika

credentials = pika.PlainCredentials("admin","admin")
connection = pika.BlockingConnection(pika.ConnectionParameters('101.133.225.166',credentials=credentials))
channel = connection.channel()

# exchange='m1',exchange(秘书)的名称
# exchange_type='direct' , 秘书工作方式将消息发送给不同的关键字
channel.exchange_declare(exchange='m3',exchange_type='topic')

# 随机生成一个队列
result = channel.queue_declare(queue='',exclusive=True)
queue_name = result.method.queue
print(queue_name)
# 让exchange和queque进行绑定.
channel.queue_bind(exchange='m3',queue=queue_name,routing_key='lqz.#')



def callback(ch, method, properties, body):
print("消费者接受到了任务: %r" % body)

channel.basic_consume(queue=queue_name,on_message_callback=callback,auto_ack=True)

channel.start_consuming()

订阅者2

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import pika

credentials = pika.PlainCredentials("admin","admin")
connection = pika.BlockingConnection(pika.ConnectionParameters('101.133.225.166',credentials=credentials))
channel = connection.channel()

# exchange='m1',exchange(秘书)的名称
# exchange_type='topic' , 模糊匹配
channel.exchange_declare(exchange='m3',exchange_type='topic')

# 随机生成一个队列
result = channel.queue_declare(queue='',exclusive=True)
queue_name = result.method.queue
print(queue_name)
# 让exchange和queque进行绑定.
channel.queue_bind(exchange='m3',queue=queue_name,routing_key='lqz.*')


def callback(ch, method, properties, body):
queue_name = result.method.queue # 发送的routing_key是什么
print("消费者接受到了任务: %r" % body)

channel.basic_consume(queue=queue_name,on_message_callback=callback,auto_ack=True)

channel.start_consuming()

十 基于rabbitmq实现rpc

服务端

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import pika
credentials = pika.PlainCredentials("admin","admin")
connection = pika.BlockingConnection(pika.ConnectionParameters('101.133.225.166',credentials=credentials))
channel = connection.channel()

# 起翰监听任务队列
channel.queue_declare(queue='rpc_queue')

def on_request(ch, method, props, body):
n = int(body)
response = n + 100
# props.reply_to 要放结果的队列.
# props.correlation_id 任务
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( queue='rpc_queue',on_message_callback=on_request,)
channel.start_consuming()

客户端

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import pika
import uuid

class FibonacciRpcClient(object):
def __init__(self):
credentials = pika.PlainCredentials("admin", "admin")
self.connection = pika.BlockingConnection(pika.ConnectionParameters('101.133.225.166', credentials=credentials))
self.channel = self.connection.channel()

# 随机生成一个消息队列(用于接收结果)
result = self.channel.queue_declare(queue='',exclusive=True)
self.callback_queue = result.method.queue

# 监听消息队列中是否有值返回,如果有值则执行 on_response 函数(一旦有结果,则执行on_response)
self.channel.basic_consume(queue=self.callback_queue,on_message_callback=self.on_response, auto_ack=True)

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

def call(self, n):
self.response = None
self.corr_id = str(uuid.uuid4())

# 客户端 给 服务端 发送一个任务: 任务id = corr_id / 任务内容 = '30' / 用于接收结果的队列名称
self.channel.basic_publish(exchange='',
routing_key='rpc_queue', # 服务端接收任务的队列名称
properties=pika.BasicProperties(
reply_to = self.callback_queue, # 用于接收结果的队列
correlation_id = self.corr_id, # 任务ID
),
body=str(n))
while self.response is None:
self.connection.process_data_events()

return self.response

fibonacci_rpc = FibonacciRpcClient()

response = fibonacci_rpc.call(50)
print('返回结果:',response)
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