python rabbitmq实现简单/持久/广播/组播/topic/rpc消息异步发送可配置Django

windows首先安装rabbitmq 点击参考安装

1、环境介绍

Python 3.10.16
其他通过pip安装的版本(Django、pika、celery这几个必须要有最好版本一致)
amqp              5.3.1
asgiref           3.8.1
async-timeout     5.0.1
billiard          4.2.1
celery            5.4.0
click             8.1.7
click-didyoumean  0.3.1
click-plugins     1.1.1
click-repl        0.3.0
colorama          0.4.6
Django            4.2
dnspython         2.7.0
eventlet          0.38.2
greenlet          3.1.1
kombu             5.4.2
pika              1.3.2
pip               24.2
prompt_toolkit    3.0.48
python-dateutil   2.9.0.post0
redis             5.2.1
setuptools        75.1.0
six               1.17.0
sqlparse          0.5.3
typing_extensions 4.12.2
tzdata            2024.2
vine              5.1.0
wcwidth           0.2.13
wheel             0.44.0

2、创建Django 项目

django-admin startproject django_rabbitmq

3、在setting最下边写上

# settings.py    guest:guest 表示的是你安装好的rabbitmq的登录账号和密码
BROKER_URL = 'amqp://guest:guest@localhost:15672/'
CELERY_RESULT_BACKEND = 'rpc://'

4.1 简单模式

4.1.1 在和setting同级的目录下创建一个叫consumer.py的消费者文件，其内容如下：

import pikadef callback(ch, method, properties, body):print(f"[x] Received {body.decode()}")def start_consuming():# 创建与RabbitMQ的连接connection = pika.BlockingConnection(pika.ConnectionParameters(host='localhost'))channel = connection.channel()# 声明一个队列channel.queue_declare(queue='hello')# 指定回调函数channel.basic_consume(queue='hello', on_message_callback=callback, auto_ack=True)print('[*] Waiting for messages. To exit press CTRL+C')channel.start_consuming()if __name__ == "__main__":start_consuming()

4.1.2 在和setting同级的目录下创建一个叫producer.py的生产者文件，其内容如下：

import pikadef publish_message():# message = request.GET.get('msg')# 创建与RabbitMQ的连接connection = pika.BlockingConnection(pika.ConnectionParameters(host='localhost'))channel = connection.channel()# 声明一个队列channel.queue_declare(queue='hello')# 发布消息message = "Hello World!"channel.basic_publish(exchange='', routing_key='hello', body=message)print(f"[x] Sent '{message}'")# 关闭连接connection.close()if __name__ == "__main__":publish_message()

4.1.3 先运行消费者代码(consumer.py)再运行生产者代码(producer.py)

先：python consumer.py
再： python producer.py

4.1.4 运行结果如下：

在这里插入图片描述

4.2 消息持久化模式

4.2.1 在和setting同级的目录下创建一个叫recv_msg_safe.py的消费者文件，其内容如下：

import time
import pikadef callback(ch, method, properties, body):print(" [x] Received %r" % body)time.sleep(20)print(" [x] Done")# 下边这个就是标记消费完成了，下次在启动接受消息就不用从头开始了，即# 手动确认消息消费完成 和auto_ack=False 搭配使用ch.basic_ack(delivery_tag=method.delivery_tag)  # method.delivery_tag就是一个标识符，方便找对人def start_consuming():# 创建与RabbitMQ的连接connection = pika.BlockingConnection(pika.ConnectionParameters(host='localhost'))channel = connection.channel()# 声明一个队列channel.queue_declare(queue='hello2', durable=True)  # 若声明过，则换一个名字# 指定回调函数channel.basic_consume(queue='hello2',on_message_callback=callback,# auto_ack=True  # 为true则不能持久话消息，即消费者关闭后下次收不到之前未收取的消息auto_ack=False  # 为False则下次依然从头开始收取消息，直到callback函数调用完成)print('[*] Waiting for messages. To exit press CTRL+C')channel.start_consuming()if __name__ == "__main__":start_consuming()

4.2.2 在和setting同级的目录下创建一个叫send_msg_safe.py的生产者文件，其内容如下：

import pikadef publish_message():# 创建与RabbitMQ的连接connection = pika.BlockingConnection(pika.ConnectionParameters(host='localhost'))channel = connection.channel()# 声明一个队列  durable=True队列持久化channel.queue_declare(queue='hello2', durable=True)channel.basic_publish(exchange='',routing_key='hello2',body='Hello World!',# 消息持久话用，主要用作宕机的时候，估计是写入本地硬盘了properties=pika.BasicProperties(delivery_mode=2,  # make message persistent))# 关闭连接connection.close()if __name__ == "__main__":publish_message()

4.2.3 先运行消费者代码(recv_msg_safe.py)再运行生产者代码(send_msg_safe.py) 执行结果如下：

在这里插入图片描述

4.3 广播模式

4.3.1 在和setting同级的目录下创建一个叫fanout_receive.py的消费者文件，其内容如下：

# 广播模式
import pika# credentials = pika.PlainCredentials('guest', 'guest')
# connection = pika.BlockingConnection(pika.ConnectionParameters(
#     host='localhost', credentials=credentials))
# 在setting中如果不配置BROKER_URL和CELERY_RESULT_BACKEND的情况下请使用上边的代码
connection = pika.BlockingConnection(pika.ConnectionParameters(host='localhost'))
channel = connection.channel()
channel.exchange_declare(exchange='logs', exchange_type='fanout')  # 指定发送类型
# 必须能过queue来收消息
result = channel.queue_declare("", exclusive=True)  # 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
queue_name = result.method.queue
channel.queue_bind(exchange='logs', queue=queue_name)  # 随机生成的Q，绑定到exchange上面。
print(' [*] Waiting for logs. To exit press CTRL+C')def callback(ch, method, properties, body):print(" [x] %r" % body)channel.basic_consume(on_message_callback=callback, queue=queue_name, auto_ack=True)
channel.start_consuming()

4.3.2 在和setting同级的目录下创建一个叫fanout_send.py的生产者文件，其内容如下：

# 通过广播发消息
import pika
import sys# credentials = pika.PlainCredentials('guest', 'guest')
# connection = pika.BlockingConnection(pika.ConnectionParameters(
#     host='localhost', credentials=credentials))
# 在setting中如果不配置BROKER_URL和CELERY_RESULT_BACKEND的情况下请使用上边的代码
connection = pika.BlockingConnection(pika.ConnectionParameters(host='localhost'))
channel = connection.channel()
channel.exchange_declare(exchange='logs', exchange_type='fanout') #发送消息类型为fanout,就是给所有人发消息# 如果等于空，就输出hello world!
message = ' '.join(sys.argv[1:]) or "info: Hello World!"channel.basic_publish(exchange='logs',routing_key='',  # routing_key 转发到那个队列，因为是广播所以不用写了body=message)print(" [x] Sent %r" % message)
connection.close()

4.3.3 先运行消费者代码(fanout_receive.py)再运行生产者代码(fanout_send.py) 执行结果如下：

在这里插入图片描述

4.4 组播模式

4.4.1 在和setting同级的目录下创建一个叫direct_recv.py的消费者文件，其内容如下：

import pika
import syscredentials = pika.PlainCredentials('guest', 'guest')
connection = pika.BlockingConnection(pika.ConnectionParameters(host='localhost', credentials=credentials))channel = connection.channel() channel.exchange_declare(exchange='direct_logs', exchange_type='direct')
result = channel.queue_declare("", exclusive=True)
queue_name = result.method.queueseverities = sys.argv[1:]   # 接收那些消息（指info，还是空），没写就报错
if not severities:sys.stderr.write("Usage: %s [info] [warning] [error]\n" % sys.argv[0])  # 定义了三种接收消息方式info,warning,errorsys.exit(1)for severity in severities:  # [error  info  warning]，循环severitieschannel.queue_bind(exchange='direct_logs',queue=queue_name,routing_key=severity)  # 循环绑定关键字
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(on_message_callback=callback, queue=queue_name,)
channel.start_consuming()

4.4.2 在和setting同级的目录下创建一个叫direct_send.py的生产者文件，其内容如下：

# 组播
import pika
import syscredentials = pika.PlainCredentials('guest', 'guest')
connection = pika.BlockingConnection(pika.ConnectionParameters(host='localhost', credentials=credentials))channel = connection.channel()channel.exchange_declare(exchange='direct_logs',exchange_type='direct') #指定类型severity = sys.argv[1] if len(sys.argv) > 1 else 'info'  #严重程序，级别；判定条件到底是info，还是空，后面接消息message = ' '.join(sys.argv[2:]) or 'Hello World!'  #消息channel.basic_publish(exchange='direct_logs',routing_key=severity, #绑定的是：error  指定关键字（哪些队列绑定了，这个级别，那些队列就可以收到这个消息）body=message)print(" [x] Sent %r:%r" % (severity, message))
connection.close()

4.4.3 先运行消费者代码(direct_recv.py)再运行生产者代码(direct_send.py) 执行结果如下：

在这里插入图片描述

4.5 更细致的topic模式

4.5.1 在和setting同级的目录下创建一个叫topic_recv.py的消费者文件，其内容如下：

import pika
import syscredentials = pika.PlainCredentials('guest', 'guest')
connection = pika.BlockingConnection(pika.ConnectionParameters(host='localhost', credentials=credentials))channel = connection.channel()
channel.exchange_declare(exchange='topic_logs',exchange_type='topic')result = channel.queue_declare("", exclusive=True)
queue_name = result.method.queuebinding_keys = sys.argv[1:]
if not binding_keys:print("sys.argv[0]", sys.argv[0])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(on_message_callback=callback,queue=queue_name)channel.start_consuming()

4.5.2 在和setting同级的目录下创建一个叫topic_send.py的生产者文件，其内容如下：

import pika
import syscredentials = pika.PlainCredentials('guest', 'guest')
connection = pika.BlockingConnection(pika.ConnectionParameters(host='localhost', credentials=credentials))channel = connection.channel()channel.exchange_declare(exchange='topic_logs',exchange_type='topic') #指定类型routing_key = sys.argv[1] if len(sys.argv) > 1 else 'anonymous.info'message = ' '.join(sys.argv[2:]) or 'Hello World!'  #消息channel.basic_publish(exchange='topic_logs',routing_key=routing_key,body=message)
print(" [x] Sent %r:%r" % (routing_key, message))
connection.close()

4.5.3 先运行消费者代码(topic_recv.py)再运行生产者代码(topic_send.py) 执行结果如下：

在这里插入图片描述

4.6 Remote procedure call (RPC) 双向模式

4.6.1 在和setting同级的目录下创建一个叫rpc_client.py的消费者文件，其内容如下：

import pika
import uuid
import time# 斐波那契数列 前两个数相加依次排列
class FibonacciRpcClient(object):def __init__(self):# 赋值变量，一个循环值self.response = None# 链接远程# self.connection = pika.BlockingConnection(pika.ConnectionParameters(#         host='localhost'))credentials = pika.PlainCredentials('guest', 'guest')self.connection = pika.BlockingConnection(pika.ConnectionParameters(host='localhost', credentials=credentials))self.channel = self.connection.channel()# 生成随机queueresult = self.channel.queue_declare("", exclusive=True)# 随机取queue名字，发给消费端self.callback_queue = result.method.queue# self.on_response 回调函数:只要收到消息就调用这个函数。# 声明收到消息后就 收queue=self.callback_queue内的消息  准备接受命令结果self.channel.basic_consume(queue=self.callback_queue,auto_ack=True, on_message_callback=self.on_response)# 收到消息就调用# ch 管道内存对象地址# method 消息发给哪个queue# body数据对象def on_response(self, ch, method, props, body):# 判断本机生成的ID 与 生产端发过来的ID是否相等if self.corr_id == props.correlation_id:# 将body值 赋值给self.responseself.response = bodydef call(self, n):# 随机一次唯一的字符串self.corr_id = str(uuid.uuid4())# routing_key='rpc_queue' 发一个消息到rpc_queue内self.channel.basic_publish(exchange='',routing_key='rpc_queue',properties=pika.BasicProperties(# 执行命令之后结果返回给self.callaback_queue这个队列中reply_to=self.callback_queue,# 生成UUID 发送给消费端correlation_id=self.corr_id,),# 发的消息，必须传入字符串，不能传数字body=str(n))# 没有数据就循环收while self.response is None:# 非阻塞版的start_consuming()# 没有消息不阻塞  检查队列里有没有新消息，但不会阻塞self.connection.process_data_events()print("no msg...")time.sleep(0.5)return int(self.response)# 实例化
fibonacci_rpc = FibonacciRpcClient()response = fibonacci_rpc.call(5)
print(" [.] Got %r" % response)

4.6.2 在和setting同级的目录下创建一个叫rpc_server.py的生产者文件，其内容如下：

#_*_coding:utf-8_*_
import pika
import time
# 链接socket
credentials = pika.PlainCredentials('guest', 'guest')
connection = pika.BlockingConnection(pika.ConnectionParameters(host='localhost', credentials=credentials))channel = connection.channel()# 生成rpc queue  在这里声明的所以先启动这个
channel.queue_declare(queue='rpc_queue')#　斐波那契数列
def fib(n):if n == 0:return 0elif n == 1:return 1else:return fib(n-1) + fib(n-2)# 收到消息就调用
# ch 管道内存对象地址
# method 消息发给哪个queue
# props 返回给消费的返回参数
# body数据对象
def on_request(ch, method, props, body):n = int(body)print(" [.] fib(%s)" % n)# 调用斐波那契函数 传入结果response = fib(n)ch.basic_publish(exchange='',# 生产端随机生成的queuerouting_key=props.reply_to,# 获取UUID唯一 字符串数值properties=pika.BasicProperties(correlation_id=props.correlation_id),# 消息返回给生产端body=str(response))# 确保任务完成# ch.basic_ack(delivery_tag = method.delivery_tag)# 每次只处理一个任务
# channel.basic_qos(prefetch_count=1)
# rpc_queue收到消息:调用on_request回调函数
# queue='rpc_queue'从rpc内收
channel.basic_consume(queue="rpc_queue",auto_ack=True,on_message_callback=on_request)
print(" [x] Awaiting RPC requests")
channel.start_consuming()