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]\n" % 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]...\n" % 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)
本文来自博客园,作者:一石数字欠我15w!!!,转载请注明原文链接:https://www.cnblogs.com/52-qq/p/8511116.html
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