Python中使用RabbitMQ

一 RabbitMQ简介

RabbitMQ是一个在AMQP基础上完整的，可复用的企业消息系统。他遵循Mozilla Public License开源协议。

MQ全称为Message Queue, 消息队列（MQ）是一种应用程序对应用程序的通信方法。应用程序通过读写出入队列的消息（针对应用程序的数据）来通信，而无需专用连接来链接它们。消 息传递指的是程序之间通过在消息中发送数据进行通信，而不是通过直接调用彼此来通信，直接调用通常是用于诸如远程过程调用的技术。排队指的是应用程序通过 队列来通信。队列的使用除去了接收和发送应用程序同时执行的要求。

二 安装RabbitMQ

Linux系统

yum install rabbitmq-server

启动服务

service rabbitmq-server start 默认端口5672

Python环境安装pika模块

pip install pika

查看当前有多少个队列并且队列中有多少消息

rabbitmqctl list_queues

三 一个简易的生产者消费者模型

生产者：

import pika

connection = pika.BlockingConnection(
    pika.ConnectionParameters('192.168.0.108')
)

channel = connection.channel()  # 声明一个管道

# 声明queue
channel.queue_declare(queue='hello queue2', durable=True)  # durable 持久化队列

channel.basic_publish(
    exchange='',
    routing_key='hello queue2',  # queue名字
    body='Hello World!',  # 消息内容
    properties=pika.BasicProperties(
        delivery_mode=2  # 使队列中的消息持久化
    )
)
print("[x] Sent 'Hello World!'")
connection.close()

消费者：

import pika

connection = pika.BlockingConnection(pika.ConnectionParameters('192.168.0.108'))

channel = connection.channel()

channel.queue_declare(queue='hello queue')

def callback(ch, method, properties, body):
    print('ch', ch)  # 管道的内存对象地址
    print('me', method)
    print('pro', properties)
    print('body', body)  # 消息内容
    print("[x] Received %r" % body)
    ch.basic_ack(delivery_tag=method.delivery_tag)  # 向生产者发送确认消息

channel.basic_qos(prefetch_count=1)  # 处理完当前这条信息再发送下一条消息，公平消息机制，这样就不会因为某些处理速度慢的机器一直收到消息而处理不完
channel.basic_consume(  # 消费信息
    callback,  # 如果收到消息，就调用CALLBACK函数来处理消息
    queue='hello queue',
    no_ack=True)
print('[*] Waiting for message. to exit press CTRL+C')

# 开始收消息
channel.start_consuming()

1、acknowledgment 消息不丢失

no-ack ＝ False，如果消费者遇到情况(its channel is closed, connection is closed, or TCP connection is lost)挂掉了，那么，RabbitMQ会重新将该任务添加到队列中。

RabbitMQ是默认开启自动应答的，这样当rabbitMQ将消息发给消费者，就会从内存中将消息删除，这样会带来一个问题，如果消费者未处理完消息而宕机，那么消息就会丢失。所以，我们将自动应答关闭，当rabbitMQ收到消费者处理完消息的回应后才会从内存中删除消息。

import pika

connection = pika.BlockingConnection(pika.ConnectionParameters(
        host='10.211.55.4'))
channel = connection.channel()

channel.queue_declare(queue='hello')

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()

消费者

2、durable 消息不丢失

rabbitMQ默认将消息存储在内存中，若rabbitMQ宕机，那么所有数据就会丢失，所以在声明队列的时候可以声明将数据持久化，但是如果已经声明了一个未持久化的队列，那么不能修改，只能将这个队列删除或重新声明一个持久化数据。

#!/usr/bin/env python
import pika

connection = pika.BlockingConnection(pika.ConnectionParameters(host='10.211.55.4'))
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()

生产者

#!/usr/bin/env python
# -*- coding:utf-8 -*-
import pika

connection = pika.BlockingConnection(pika.ConnectionParameters(host='10.211.55.4'))
channel = connection.channel()

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

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()

消费者

3、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。

channel.basic_qos(prefetch_count=1) 表示谁来谁取，不再按照奇偶数排列， 处理完当前这条信息再发送下一条消息，公平消息机制，这样就不会因为某些处理速度慢的机器一直收到消息而处理不完

import pika

connection = pika.BlockingConnection(pika.ConnectionParameters('192.168.0.108'))

channel = connection.channel()

channel.queue_declare(queue='hello queue')

def callback(ch, method, properties, body):
    print('ch', ch)  # 管道的内存对象地址
    print('me', method)
    print('pro', properties)
    print('body', body)  # 消息内容
    print("[x] Received %r" % body)
    ch.basic_ack(delivery_tag=method.delivery_tag)  # 向生产者发送确认消息

channel.basic_qos(prefetch_count=1)  # 处理完当前这条信息再发送下一条消息，公平消息机制，这样就不会因为某些处理速度慢的机器一直收到消息而处理不完
channel.basic_consume(  # 消费信息
    callback,  # 如果收到消息，就调用CALLBACK函数来处理消息
    queue='hello queue',
    no_ack=True)
print('[*] Waiting for message. to exit press CTRL+C')

# 开始收消息
channel.start_consuming()

消费者

四 消息发布与订阅

之前的例子基本都是1对1的消息发送和接收，即消息只能发送到指定的queue里,但有些时候你想让你的消息被所有的Queue收到,类似广播的效果,这时候就要用到exchange了

Exchange在定义的时候是有类型的，以决定到底哪些Queue符合条件。

fanout：所有bind到此exchange的queue都可以接收消息
 direct：通过routingKey和exchage决定的那个唯一的queue可接收消息
  topic：所有符合routingKey(此时可以说一个表达式)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
　　   注意：使用RoutingKey为#， Exchange Type为topic的时候相当于fanout

headers： 通过headers来决定把消息发给哪些queue

1 fanout

RabbitMQ实现发布和订阅时，会为每一个订阅者创建一个队列，而发布者发布消息时，会将消息放置在所有相关队列中。

import pika
import sys

connection = pika.BlockingConnection(pika.ConnectionParameters(
    host='192.168.0.108'))

channel = connection.channel()

channel.exchange_declare(exchange='logs',
                         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)

生产者

import pika

connection = pika.BlockingConnection(pika.ConnectionParameters(host='192.168.0.108'))

chanel = connection.channel()

chanel.exchange_declare(exchange='logs',
                        exchange_type='fanout')

# 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
result = chanel.queue_declare(exclusive=True)

# 拿到随机的queue名字
queue_name = result.method.queue
print(queue_name)

chanel.queue_bind(exchange='logs',
                  queue=queue_name)

def callback(ch, method, properties, body):
    print(body)

chanel.basic_consume(
    callback,
    queue=queue_name,
    no_ack=True
)
print('[*] Waiting for message. to exit press CTRL+C')

chanel.start_consuming()

消费者

2 direct模式

之前事例，发送消息时明确指定某个队列并向其中发送消息，RabbitMQ还支持根据关键字发送，即：队列绑定关键字，发送者将数据根据关键字发送到消息exchange，exchange根据 关键字 判定应该将数据发送至指定队列。

import pika
import sys

connection = pika.BlockingConnection(pika.ConnectionParameters(host='192.168.0.108'))

chanel = connection.channel()

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

severity = sys.argv[1] if len(sys.argv) > 1 else 'info'
message = ' '.join(sys.argv[2:]) or 'Hello World'

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

生产者

import pika
import sys

connection = pika.BlockingConnection(pika.ConnectionParameters(host='192.168.0.108'))

chanel = connection.channel()

chanel.exchange_declare(exchange='logs',
                        exchange_type='fanout')

result = chanel.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:
    chanel.queue_bind(exchange='direct_logs',
                      queue=queue_name,
                      routing_key=severity)

def callback(ch, method, properties, body):
    print("[x] %r:%r" % (method.routing_key, body))

chanel.basic_consume(callback,
                     queue=queue_name,
                     no_ack=True)

print('[*] Waiting for message. to exit press CTRL+C')
chanel.start_consuming()

消费者

3  topic模式

在topic类型下，可以让队列绑定几个模糊的关键字，之后发送者将数据发送到exchange，exchange将传入”路由值“和 ”关键字“进行匹配，匹配成功，则将数据发送到指定队列。

• # 表示可以匹配 0 个 或 多个 单词
• *  表示只能匹配 一个 单词

import pika
import sys

connection = pika.BlockingConnection(pika.ConnectionParameters(host='192.168.0.108'))

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()

生产者

import pika
import sys

connection = pika.BlockingConnection(pika.ConnectionParameters(host='192.168.0.108'))

chanel = connection.channel()

chanel.exchange_declare(exchange='topic_logs',
                        exchange_type='topic')

result = chanel.queue_declare(exclusive=True)
queue_name = result.method.queue

binding_keys = sys.argv[1:]
if not binding_keys:
    sys.stderr.write("Usage:%s [binding_keys]\n" % sys.argv[0])
    sys.exit(1)

for severity in binding_keys:
    chanel.queue_bind(exchange='topic_logs',
                      queue=queue_name,
                      routing_key=severity)

def callback(ch, method, properties, body):
    print("[x] %r:%r" % (method.routing_key, body))

chanel.basic_consume(callback,
                     queue=queue_name,
                     no_ack=True)

print('[*] Waiting for message. to exit press CTRL+C')
chanel.start_consuming()

消费者

五 Remote procedure call (RPC)

To illustrate how an RPC service could be used we're going to create a simple client class. It's going to expose a method named call which sends an RPC request and blocks until the answer is received:

fibonacci_rpc = FibonacciRpcClient()
result = fibonacci_rpc.call(4)
print("fib(4) is %r" % result)

RPC server:

import pika
import time

connection = pika.BlockingConnection(pika.ConnectionParameters(host="192.168.0.108"))
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')

RPC client

import pika
import uuid

class FibonacciRpcClient(object):
    def __init__(self):
        self.connection = pika.BlockingConnection(pika.ConnectionParameters(host='192.168.0.108'))
        self.channel = self.connection.channel()
        result = self.channel.queue_declare(exclusive=True)
        self.callback_queue = result.method.queue
        self.channel.basic_consume(self.on_response, no_ack=True,
                                   queue=self.callback_queue)
        self.response = None

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

    def call(self, n):

        self.corr_id = str(uuid.uuid4())
        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)
                                   )

        while self.response is None:
            self.connection.process_data_events()
        return int(self.response)

fibonacci_rpc = FibonacciRpcClient()
print("[X]Requesting fib")
response = fibonacci_rpc.call(30)
print("[.]Got %r" % response)

print("[X] Awaiting RPC request")
channel.start_consuming()