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RabbitMQ中RPC的实现及其通信机制

rengzhixin 2019-01-20 16:17 1101 查看 https://www.cnblogs.com/FG123/

RabbitMQ中RPC的实现:客户端发送请求消息,服务端回复响应消息,为了接受响应response,客户端需要发送一个回调队列的地址来接受响应,每条消息在发送的时候会带上一个唯一的correlation_id,相应的服务端处理计算后会将结果返回到对应的correlation_id。

RPC调用流程:

当生产者启动时,它会创建一个匿名的独占回调队列,对于一个RPC请求,生产者发送一条具有两个属性的消息:reply_to(回调队列),correlation_id(每个请求的唯一值),请求被发送到rpc_queue队列,消费者等待该队列上的请求。当一个请求出现时,它会执行该任务,将带有结果的消息发送回生产者。生产者等待回调队列上的数据,当消息出现时,它检查相关ID属性,如果它与请求中的值匹配,则返回对应用程序的响应。

 RabbitMQ斐波拉契计算的RPC,消费者实现:

"""
基于RabbitMQ实现RPC通信机制 --> 服务端
"""

import pika
import uuid
from functools import lru_cache

class RabbitServer(object):
def __init__(self):
self.conn = pika.BlockingConnection(
pika.ConnectionParameters(host='localhost', port=5672)
)
self.channel = self.conn.channel()

# 声明一个队列,并进行持久化,exclusive设置为false
self.channel.queue_declare(
exclusive=False, durable=True, queue='task_queue'
)

# 声明一个exhange交换机,类型为topic
self.channel.exchange_declare(
exchange='logs_rpc', exchange_type='topic', durable=True
)

# 将队列与交换机进行绑定
routing_keys = ['#']  # 接受所有的消息
for routing_key in routing_keys:
self.channel.queue_bind(
exchange='logs_rpc', queue='task_queue', routing_key=routing_key
)

@lru_cache()
def fib(self, n):
"""
斐波那契数列.===>程序的处理逻辑
使用lru_cache 优化递归
:param n:
:return:
"""
if n == 0:
return 0
elif n == 1:
return 1
else:
return self.fib(n - 1) + self.fib(n - 2)

def call_back(self, channel, method, properties, body):
print('------------------------------------------')
print('接收到的消息为(斐波那契数列的入参项为):{}'.format(str(body)))
print('消息的相关属性为:')
print(properties)
value = self.fib(int(body))
print('斐波那契数列的运行结果为:{}'.format(str(value)))

# 交换机将消息发送到队列
self.channel.basic_publish(
exchange='',
routing_key=properties.reply_to,
body=str(value),
properties=pika.BasicProperties(
delivery_mode=2,
correlation_id=properties.correlation_id,
))

# 消费者对消息进行确认
self.channel.basic_ack(delivery_tag=method.delivery_tag)

def receive_msg(self):
print('开始接受消息...')
self.channel.basic_qos(prefetch_count=1)
self.channel.basic_consume(
consumer_callback=self.call_back,
queue='task_queue',
no_ack=False,  # 消费者对消息进行确认
consumer_tag=str(uuid.uuid4())
)

def consume(self):
self.receive_msg()
self.channel.start_consuming()

if __name__ == '__main__':
rabbit_consumer = RabbitServer()
rabbit_consumer.consume()

 生产者实现:

"""
基于RabbitMQ实现RPC通信机制 --> 客户端
"""

import pika
import uuid
import time

class RabbitClient(object):
def __init__(self):
# 与RabbitMq服务器建立连接
self.conn = pika.BlockingConnection(
pika.ConnectionParameters(host='localhost', port=5672)
)
self.channel = self.conn.channel()

# 声明一个exchange交换机,交换机的类型为topic
self.channel.exchange_declare(
exchange='logs_rpc', exchange_type='topic', durable=True
)

# 声明一个回调队列,用于接受RPC回调结果的运行结果
result = self.channel.queue_declare(durable=True, exclusive=False)
self.call_queue = result.method.queue

# 从回调队列当中获取运行结果.
self.channel.basic_consume(
consumer_callback=self.on_response,
queue=self.call_queue,
no_ack=False
)

def on_response(self, channel, method, properties, body):
"""
对收到的消息进行确认
找到correlation_id与服务端的消息标识匹配的消息结果
:param channel:
:param method:
:param properties:
:param body:
:return:
"""
if self.corr_id == properties.correlation_id:
self.response = body
print('斐波那契数列的RPC返回结果是:{}'.format(body))
print('相关属性信息:')
print(properties)
self.channel.basic_ack(delivery_tag=method.delivery_tag)

def send_msg(self, routing_key, message):
"""
exchange交换机将根据消息的路由键将消息路由到对应的queue当中
:param routing_key: 消息的路由键
:param message: 生成者发送的消息
:return:
"""
self.response = None
self.corr_id = str(uuid.uuid4())
self.channel.basic_publish(
exchange='logs_rpc',
routing_key=routing_key,
body=message,
properties=pika.BasicProperties(
delivery_mode=2,
correlation_id=self.corr_id,
reply_to=self.call_queue,
))

while self.response is None:
print('等待远程服务端的返回结果...')
self.conn.process_data_events()  # 非阻塞式的不断获取消息.

return self.response

def close(self):
self.conn.close()

if __name__ == "__main__":
rabbit_producer = RabbitClient()
routing_key = 'hello every one'
start_time = int(time.time())
for item in range(2000):
num = str(item)
print('生产者发送的消息为:{}'.format(num))
rabbit_producer.send_msg(routing_key, num)
end_time = int(time.time())
print("耗时{}s".format(str(end_time - start_time)))

计算2000以内的斐波拉契数列,执行结果如下:

 

 

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