RabbitMQ的工作模式

#!/usr/bin/env python
import pika
import json

from callback import callback


class RabbitQueue:
    def __init__(self):
        self.channel = None

    def connect(self):
        credit = pika.PlainCredentials(username='admin', password='admin')
        self.channel = pika.BlockingConnection(pika.ConnectionParameters(host='192.168.3.19', port=5672, credentials=credit)).channel()

    @staticmethod
    def callback(channel, method, properties, body):
        """callback函数需要自定义：返回结果：body为消息队列获取结果"""
        receive = body.decode()
        print(channel.__dict__)
        print(receive)
        print(method)
        print(properties)
        channel.basic_ack(delivery_tag=method.delivery_tag)

    def image_enqueue(self, queue_name, image_list):
        """推送数据至Rabbitmq消息队列"""
        self.connect()
        channel = self.channel
        channel.queue_declare(queue=queue_name, durable=True)  # 声明RPC请求队列,durable=True队列持久化
        channel.basic_publish(
            exchange='',
            routing_key=queue_name,
            body=json.dumps(image_list, ensure_ascii=False),
            properties=pika.BasicProperties(
                delivery_mode=2,  # 消息持久化
            )
        )
        channel.close()

    def bpop_queue(self, queue_name, timeout=0):
        """从消息队列获取数据"""
        self.connect()
        channel = self.channel
        channel.queue_declare(queue=queue_name, durable=True)
        # 需要自定义callback函数
        channel.basic_consume(on_message_callback=callback, queue=queue_name, auto_ack=False)
        channel.start_consuming()


if __name__ == '__main__':
    obj = RabbitQueue()
    obj.image_enqueue(queue_name='_device_image_', image_list='hello world')

    obj.bpop_queue(queue_name='_device_image_')  # 阻塞等待

 

简单模式：

# #########################基于简单模式的 生产者 #########################
#!/usr/bin/env python
import pika

# 封装 socket通信实现
connection = pika.BlockingConnection(pika.ConnectionParameters(host='192.168.253.128',port=5672))

# 创建通道对象
channel = connection.channel()

# 创建一个队列：名字是hello
channel.queue_declare(queue='hello')

# 向队列hello里丢东西
channel.basic_publish(exchange='',
                      routing_key='hello',
                      body='Hello World!')

print(" [x] Sent 'Hello World!'")
connection.close()

# ##########################基于简单模式的 消费者 ##########################
import pika


connection = pika.BlockingConnection(pika.ConnectionParameters(host='192.168.253.128',port=5672))
channel = connection.channel()

channel.queue_declare(queue='hello')


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

# 如果能从hello这个队列获取到数据就执行callback，否则继续往下走
channel.basic_consume(callback,
                      queue='hello',
                      no_ack=True)
no_ack参数：如果为False，当消费者服务器挂掉了，那么rabbitmq会重新将该任务添加到任务队列中

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

此时服务端的代码可以这么写：

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()

durable模式：信息不丢失
# 生产者
#!/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()


# 消费者
#!/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

---

enchange模型

一、发布订阅模式（fanout）

发布订阅和简单的消息队列区别在于，发布订阅会将消息发送给所有的订阅者，而消息队列中的数据被消费一次便消失。所以，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。

# 生产者
#!/usr/bin/env python
import pika
import sys

connection = pika.BlockingConnection(pika.ConnectionParameters(
        host='localhost'))
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)
connection.close()


# 消费者
#!/usr/bin/env python
import pika

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

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

result = channel.queue_declare(exclusive=True)
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,
                      no_ack=True)

channel.start_consuming()

二、关键字模式（direct）

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。

#!/usr/bin/env python
import pika
import sys

connection = pika.BlockingConnection(pika.ConnectionParameters(
        host='localhost'))
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] %r:%r" % (method.routing_key, body))

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

channel.start_consuming()

三、模糊匹配（topic）

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。

#!/usr/bin/env python
import pika
import sys

connection = pika.BlockingConnection(pika.ConnectionParameters(
        host='localhost'))
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] %r:%r" % (method.routing_key, body))

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

channel.start_consuming()

 基于rabbitmq的RPC

#!/usr/bin/env python 服务端
import pika

# 建立连接，服务器地址为localhost，可指定ip地址
connection = pika.BlockingConnection(pika.ConnectionParameters(
    host='192.168.253.128', port=5672))

# 建立会话
channel = connection.channel()

# 声明RPC请求队列
channel.queue_declare(queue='rpc_queue')


# 数据处理方法
def fib(n):
    if n == 0:
        return 0
    elif n == 1:
        return 1
    else:
        return fib(n - 1) + fib(n - 2)


# 对RPC请求队列中的请求进行处理
def on_request(ch, method, props, body):
    n = int(body)

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

    # 调用数据处理方法
    response = fib(n)

    # 将处理结果(响应)发送到回调队列
    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)


# 负载均衡，同一时刻发送给该服务器的请求不超过一个
channel.basic_qos(prefetch_count=1)

channel.basic_consume(on_request, queue='rpc_queue')

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

#!/usr/bin/env python
import pika
import uuid

class FibonacciRpcClient(object):
    def __init__(self):
        # 建立连接，指定服务器的ip地址
        self.connection = pika.BlockingConnection(pika.ConnectionParameters(
            host='192.168.253.128', port=5672))

        # 建立一个会话，每个channel代表一个会话任务
        self.channel = self.connection.channel()

        # 声明回调队列，再次声明的原因是，服务器和客户端可能先后开启，该声明是幂等的，多次声明，但只生效一次
        result = self.channel.queue_declare(exclusive=True)
        # 将次队列指定为当前客户端的回调队列
        self.callback_queue = result.method.queue

        # 客户端订阅回调队列，当回调队列中有响应时，调用`on_response`方法对响应进行处理;
        self.channel.basic_consume(self.on_response, no_ack=True,
                                   queue=self.callback_queue)

    # 对回调队列中的响应进行处理的函数
    def on_response(self, ch, method, props, body):
        if self.corr_id == props.correlation_id:
            self.response = body

    # 发出RPC请求
    def call(self, n):

        # 初始化 response
        self.response = None

        # 生成correlation_id
        self.corr_id = str(uuid.uuid4())

        # 发送RPC请求内容到RPC请求队列`rpc_queue`，同时发送的还有`reply_to`和`correlation_id`
        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)

# 建立客户端
fibonacci_rpc = FibonacciRpcClient()

# 发送RPC请求
print("开始发送数据")
response = fibonacci_rpc.call(30)
print(" [.] Got %r" % response)