说明

通过rabbit_agent, 以接口方式实现对队列的标准操作，将pika包在微服务内，而不必在太多地方重复的去写。至少在服务端发布消息时，不必再去考虑这些问题。

在分布式任务的情况下，客户端本身会启动一个持续监听队列的客户端服务，这些应该是容易通过简单的配置来实现的。

在未来的应用上，我计划使用rabbitmq作为公网的消息队列，满足分布式计算的要求。例如，我部署了n个大模型，希望它们可以处理接口请求。很显然，一台服务器放不下n个大模型，但是用户可以把请求先发到消息队列，然后在不同的机器上启动大模型，分别接受来自队列的消息，处理后返回。
这样，只要在服务端有一个前端，可以转发、收集消息展示在应用前端，那么就可行了。

内容

模式1：简单队列，连通性测试

适合简单缓存

有P(Produce)和C(Consumer)两端。

生产端在建立连接后，声明队列，然后往里面发消息。
Connection -> Channel -> Queue -> Message

生产

生产者：将消息发送到队列。
模式：只有在有新的消息要发布时才连接队列。(然后就可以释放连接）

import pikacredentials = pika.PlainCredentials('xxx', 'xxx')import timewith pika.BlockingConnection(pika.ConnectionParameters('HOST', 11111, '/', credentials)) as connection:channel = connection.channel()channel.queue_declare(queue='hello')# 方式一：基本队列for i in range(100):time.sleep(0.1)channel.basic_publish(exchange='',routing_key='hello',body='Hello World!')print(" [x] Sent 'Hello World!'")

消费

消费者：将消息提取出来并打印。
模式：一直处于监听状态，所以连接需要一直保持。

def callback(ch, method, properties, body):print(f" [x] Received {body}")connection = pika.BlockingConnection(pika.ConnectionParameters('HOST', 11111, '/', credentials))channel = connection.channel()channel.queue_declare(queue='hello')# 方式一：基本队列channel.basic_consume(queue='hello',auto_ack=True,on_message_callback=callback)print(' [*] Waiting for messages. To exit press CTRL+C')channel.start_consuming()

模式2：工作队列，区分消费者

适合分布式任务

这个模式下，稍微有点复杂。

简单模式生产者：

import sysmessage = ' '.join(sys.argv[1:]) or "Hello World!"channel.basic_publish(exchange='',routing_key='hello',body=message)print(f" [x] Sent {message}")

如果这时候rabbitmq挂了，那么数据就会丢失，这时候要在声明队列时声明持久化的。但这要求队列在一开始就声明是持久化的。如果队列一开始没声明，再次声明会报错。
同时在发布消息的时候，也要声明持久化

channel.queue_declare(queue='hello', durable=True)channel.basic_publish(exchange='',routing_key="hello1",body=message,properties=pika.BasicProperties(delivery_mode = pika.DeliveryMode.Persistent))

配置完重启服务测试

docker restart rabbitmq_24091_24092

ok，生产端的消息被持久化了，即使重启消息也没有丢失。

接下来就是客户端。也就是worker。
考虑到worker同样存在不可靠的情况，有可能消息消费到一半，然后worker挂了。所以这里主要是消息的应答机制。

默认情况下，worker采用自动应答机制。即获取消息就认为被正常消费。这适用于产品的稳定性很高，或者消息的重要性很低的情况（允许漏消息）。

def callback(ch, method, properties, body):print(f" [x] Received {body.decode()}")time.sleep(body.count(b'.'))print(" [x] Done")# 方式一：基本队列channel.basic_consume(queue='hello',on_message_callback=callback, auto_ack =True)    

如果要做更可靠的确认，可以采取这种手工应答的机制。即消费时声明不自动确认，然后在callback内部确认。

# 手动确认def callback(ch, method, properties, body):print(f" [x] Received {body.decode()}")time.sleep(body.count(b'.'))print(" [x] Done")ch.basic_ack(delivery_tag = method.delivery_tag)channel.basic_consume(queue='hello',on_message_callback=callback, auto_ack =False)    print(' [*] Waiting for messages. To exit press CTRL+C')channel.start_consuming()

最后是负载均衡，在这里，通过消费者声明自己的预取数量来完成。

channel.basic_qos(prefetch_count=3)channel.basic_consume(queue='hello1',on_message_callback=callback, auto_ack =False)    

3 广播

我认为在复杂决策场景下可以用到。

emit_log.py

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(f" [x] Sent {message}")connection.close()'''如您所见，建立连接后我们声明了交换。此步骤是必要的，因为禁止发布到不存在的交易所。如果还没有队列绑定到交换器，消息将会丢失，但这对我们来说没关系；如果还没有消费者在监听，我们可以安全地丢弃该消息。python3 emit_log.py First message.'''

receive_logs1.py

channel = connection.channel()channel.exchange_declare(exchange='logs', exchange_type='fanout')result = channel.queue_declare(queue='', exclusive=True)queue_name = result.method.queuechannel.queue_bind(exchange='logs', queue=queue_name)print(' [*] Waiting for logs. To exit press CTRL+C')def callback(ch, method, properties, body):print(f" [x] {body}")channel.basic_consume(queue=queue_name, on_message_callback=callback, auto_ack=True)channel.start_consuming()'''python3 receive_logs1.py >> logs_from_rabbit1.logpython3 receive_logs2.py >> logs_from_rabbit2.log'''

在两个终端分别执行

python3 receive_logs1.py >> logs_from_rabbit1.logpython3 receive_logs2.py >> logs_from_rabbit2.log

当关闭后，数据被写入日志

队列被自动删除

4 路由

fanout是无意识转发，direct可以通过不同的路由键值决定队列分发，或者消息丢弃（如严重程度低的）。这种过滤和转发是通过路由键来确定的 routing_key

5 主题

有点像正则，实现更复杂的过滤。

5 微服务

命名为rabbit_agent_24098，第一步先实现模式二(worker)和模式三(subscribe)

先获取到基本包，并安装，能省不少事

wget  Basefuncs-1.2-py3-none-any.whl pip install Basefuncs-1.2-py3-none-any.whl

然后是简单的server_funcs.py,在server_funcs里定义了两个基础文件夹(现在看来也不是特别需要)

# 【创建tornado所需问文件夹】import os# 如果路径不存在则创建def create_folder_if_notexist(somepath):if not os.path.exists(somepath):os.makedirs(somepath)return Truem_static = os.path.join(os.getcwd(),'m_static')m_template = os.path.join(os.getcwd(),'m_template')create_folder_if_notexist(m_static)create_folder_if_notexist(m_template)settings = {'static_path':m_static,'template_path':m_template}# 如果需要序列化含np的内容import jsonfrom json import JSONEncoderclass MyEncoder(JSONEncoder):def default(self, obj):if isinstance(obj, np.integer):return int(obj)elif isinstance(obj, np.floating):return float(obj)elif isinstance(obj, np.ndarray):return obj.tolist()if isinstance(obj, datetime):return obj.__str__()if isinstance(obj, dd.timedelta):return obj.__str__()else:return super(MyEncoder, self).default(obj)# json.dumps(foo, cls=MyJsonEncoder)from Basefuncs import * # 读取配置conf_dict = get_conf_dict('configs.conf')

服务端：

from server_funcs import *import tornado.httpserver  # http服务器import tornado.ioloop  # ?import tornado.options  # 指定服务端口和路径解析import tornado.web  # web模块from tornado.options import define, optionsimport os.path  # 获取和生成template文件路径import pikaimport json# 全局配置文件# rabbit01 = conf_dict['rabbit01']# 应用列表app_list = []IndexHandler_path = r'/'class IndexHandler(tornado.web.RequestHandler):def get(self):self.write('【GET】This is Website for Internal API System')self.write('Please Refer to API document')print('Get got a request test')# print(buffer_dict)def post(self):request_body = self.request.bodyprint('Trying Decode Json')some_dict = json.loads(request_body)print(some_dict)msg_dict = {}msg_dict['info'] = '【POST】This is Website for Internal API System'msg_dict['input_dict'] = some_dictself.write(json.dumps(msg_dict))print('Post got a request test')IndexHandler_tuple = (IndexHandler_path,IndexHandler)app_list.append(IndexHandler_tuple)# 发布消息：传入对应的队列服务器名称，获取对应的服务器配置，然后进行连接并发布消息## 模式二：Work QueuesWorkQMessageHandler_path = r'/send_workq_message/'class WorkQMessageHandler(tornado.web.RequestHandler):def post(self):request_body = self.request.bodysome_dict = json.loads(request_body)# 1 第一层rabbit = some_dict['rabbit']exchange = some_dict.get('exchange') or ''exchange_type = some_dict.get('exchange_type') or ''# queue不可缺少queue = some_dict['queue']durable = some_dict.get('durable') if durable is None:durable = True routing_key = some_dict.get('routing_key') or ''message_list = some_dict['message_list']print(some_dict)the_rabbit_conf_dict = conf_dict[rabbit]credentials = pika.PlainCredentials(the_rabbit_conf_dict['user'], the_rabbit_conf_dict['pwd'])msg_dict = {}with pika.BlockingConnection(pika.ConnectionParameters(the_rabbit_conf_dict['host'], the_rabbit_conf_dict['port'], '/', credentials)) as connection:channel = connection.channel()if len(exchange.strip())>1:channel.exchange_declare(exchange=exchange, exchange_type=exchange_type)# 队列的持久化与否要一开始设置好if durable is True:channel.queue_declare(queue=queue, durable=True)for message in message_list:print('a :',message )channel.basic_publish(exchange= exchange, routing_key=routing_key, body=json.dumps(message),properties=pika.BasicProperties(delivery_mode = pika.DeliveryMode.Persistent))msg_dict['durable'] = Truemsg_dict['status'] = Trueelse:channel.queue_declare(queue=queue)for message in message_list:print('b :',message )channel.basic_publish(exchange= exchange, routing_key=routing_key, body=json.dumps(message) )msg_dict['durable'] = Falsemsg_dict['status'] = Truemsg_dict['messages'] = len(message_list)self.write(json.dumps(msg_dict))WorkQMessageHandler_tuple = (WorkQMessageHandler_path,WorkQMessageHandler)app_list.append(WorkQMessageHandler_tuple)## 模式三：Publish/Subscribe## 模式二：Work QueuesSubscribeMessageHandler_path = r'/send_subscribe_message/'class SubscribeMessageHandler(tornado.web.RequestHandler):def post(self):request_body = self.request.bodysome_dict = json.loads(request_body)# 1 第一层rabbit = some_dict['rabbit']exchange = some_dict.get('exchange') or ''exchange_type = some_dict.get('exchange_type') or ''routing_key = some_dict.get('routing_key') or ''message_list = some_dict['message_list']print(some_dict)the_rabbit_conf_dict = conf_dict[rabbit]credentials = pika.PlainCredentials(the_rabbit_conf_dict['user'], the_rabbit_conf_dict['pwd'])msg_dict = {}with pika.BlockingConnection(pika.ConnectionParameters(the_rabbit_conf_dict['host'], the_rabbit_conf_dict['port'], '/', credentials)) as connection:channel = connection.channel()if len(exchange.strip())>1:channel.exchange_declare(exchange=exchange, exchange_type=exchange_type)for message in message_list:channel.basic_publish(exchange= exchange, routing_key=routing_key, body=json.dumps(message))            SubscribeMessageHandler_tuple = (SubscribeMessageHandler_path,SubscribeMessageHandler)app_list.append(SubscribeMessageHandler_tuple)if __name__ == '__main__':#tornado.options.parse_command_line()apps = tornado.web.Application(app_list, **settings)http_server = tornado.httpserver.HTTPServer(apps)define('port', default=8000, help='run on the given port', type=int)http_server.listen(options.port)# 单核# 多核打开注释# 0 是全部核# http_server.start(num_processes=10) # tornado将按照cpu核数来fork进程# ---启动print('Server Started')tornado.ioloop.IOLoop.instance().start()

本地开发测试完之后，发布为镜像，然后启动服务。

docker run -d \--restart=always \--name=rabbit_agent_24098 \-v /etc/localtime:/etc/localtime  \-v /etc/timezone:/etc/timezone\-v /etc/hostname:/etc/hostname\-e "LANG=C.UTF-8" \-w /workspace\-p 24098:8000\myregistry.domain.com:24052/server.andy.rabbit_agent_24098:v100 \sh -c "python3 server.py"

模式二测试：WorkerQ

在生产端发送消息。声明了一个不持久化的队列，然后发送消息列表。注意：如果生产端声明非持久队列，那么消费端也要做同样的声明。否则会出现声明错误。另，如果消息ACK失败，RabbitMQ会在TTL之后将消息放回队列。如果消费者的通道断开连接，那么RabbitMQ也会将消息放回队列。

import requests as req message_list = [{'msg_id':1,'msg':'first msg'},{'msg_id':2,'msg':'second msg'}]# 1 模式2 WorkQ：服务端发送消息para_dict = {}para_dict['rabbit'] = 'rabbit01'para_dict['routing_key'] = 'hello2'para_dict['durable'] = Falsepara_dict['message_list'] = message_listpara_dict['queue'] = 'hello2'# res = req.post('http://127.0.0.1:8000/send_workq_message/', json = para_dict)res = req.post('http://WAN_IP:24098/send_workq_message/', json = para_dict)

在消费端执行消费。默认的情况下，body里存放的是二进制字符串。以下采取了自动和手动方式进行消息确认。

import pikaimport jsoncredentials = pika.PlainCredentials('xxx', 'xxx')connection = pika.BlockingConnection(pika.ConnectionParameters('HOST', PORT, '/', credentials))channel = connection.channel()import time# 自动确认# def callback(ch, method, properties, body):#     print(f" [x] Received {body.decode()}")#     time.sleep(body.count(b'.'))#     print(" [x] Done")# # 方式一：基本队列# channel.basic_consume(queue='hello',#                         on_message_callback=callback, auto_ack =True)    # 手动确认# def callback(ch, method, properties, body):#     input_data = json.loads(body.decode())#     print(f" [x] Received ",input_data)#     # time.sleep(body.count(b'.'))#     print(" [x] Done")#     ch.basic_ack(delivery_tag = method.delivery_tag)def callback(ch, method, properties, body):# input_data = json.loads(body.decode())print(f" [x] Received ",body.decode())# time.sleep(body.count(b'.'))print(" [x] Done")ch.basic_ack(delivery_tag = method.delivery_tag)# channel.queue_declare(queue='hello1')channel.queue_declare(queue='hello1',durable=True)channel.basic_qos(prefetch_count=3)channel.basic_consume(queue='hello1',on_message_callback=callback, auto_ack =False)    print(' [*] Waiting for messages. To exit press CTRL+C')channel.start_consuming()

模式三测试：订阅模式

para_dict = {}para_dict['rabbit'] = 'rabbit01'para_dict['routing_key'] = Nonepara_dict['durable'] = Falsepara_dict['message_list'] = message_listpara_dict['exchange'] = 'logs'para_dict['exchange_type'] = 'fanout'res = req.post('http://127.0.0.1:8000/send_subscribe_message/', json = para_dict)

订阅的worker用了另一种形式:使用系统分配的默认队列，使用完之后自动删除

#!/usr/bin/env pythonimport pikacredentials = pika.PlainCredentials(xxx, xxx)connection = pika.BlockingConnection(pika.ConnectionParameters(WAN_IP, PORT, '/', credentials))channel = connection.channel()channel.exchange_declare(exchange='logs', exchange_type='fanout')result = channel.queue_declare(queue='', exclusive=True)queue_name = result.method.queuechannel.queue_bind(exchange='logs', queue=queue_name)print(' [*] Waiting for logs. To exit press CTRL+C')def callback(ch, method, properties, body):input_data = json.loads(body.decode())print(f" [x] ",input_data)channel.basic_consume(queue=queue_name, on_message_callback=callback, auto_ack=True)channel.start_consuming()

6 结束

到这里第一版就算完成了，可以开始先用RabbitMQ做一些应用。

目前能想到的是用于分布式任务，队列中存放任务的元信息。Worker可以通过直接或者间接方式取数。
直接方式是指worker直接发起数据库的拉取动作，获得数据然后执行。
间接方式则是worker向指定队列反馈消息，由另一个服务来分发数据文件（针对租用算力机没有额外端口的情况）

在应用上，可以

• 1 为任务搭建具有前端的微服务，数据量不大，可以通过RabbitMQ直接传数据
• 2 接受来自量化程序的交易消息