rabbitmq作为消息队列可以有消息消费确认机制，之前写个基于redis的通用生产者 消费者 并发框架，redis的list结构可以简单充当消息队列，但不具备消费确认机制，随意关停程序，会丢失一部分正在程序中处理但还没执行完的消息。基于redis的与基于rabbitmq相比对消息消费速度和消息数量没有天然的支持。
使用rabbitmq的最常用库pika
不管是写代码还是运行起来都比celery使用更简单，基本能够满足绝大多数场景使用，用来取代celery worker模式（celery有三个模式，worker模式最常用，其余是定时和间隔时间两种模式）的后台异步的作用。
# coding=utf-8
"""
一个通用的rabbitmq生产者和消费者。使用多个线程消费同一个消息队列。
"""
from collections import Callable
import functools
import time
from threading import Lock
from pika import BasicProperties
# noinspection PyUnresolvedReferences
from app.utils_ydf import (LoggerMixin, LogManager, decorators, RabbitMqHelper, BoundedThreadPoolExecutor) class RabbitmqPublisher(LoggerMixin):
def __init__(self, queue_name, log_level_int=1):
self._queue_name = queue_name
self.logger.setLevel(log_level_int * 10)
channel = RabbitMqHelper().creat_a_channel()
channel.queue_declare(queue=queue_name, durable=True)
self.channel = channel
self.lock = Lock()
self._current_time = None
self.count_per_minute = None
self._init_count()
self.logger.info(f'{self.__class__} 被实例化了') def _init_count(self):
self._current_time = time.time()
self.count_per_minute = 0 def publish(self, msg):
with self.lock: # 亲测pika多线程publish会出错。
self.channel.basic_publish(exchange='',
routing_key=self._queue_name,
body=msg,
properties=BasicProperties(
delivery_mode=2, # make message persistent
)
)
self.logger.debug(f'放入 {msg} 到 {self._queue_name} 队列中')
self.count_per_minute += 1
if time.time() - self._current_time > 60:
self._init_count()
self.logger.info(f'一分钟内推送了 {self.count_per_minute} 条消息到 {self.channel.connection} 中') class RabbitmqConsumer(LoggerMixin):
def __init__(self, queue_name, consuming_function: Callable = None, threads_num=100, max_retry_times=3, log_level=1, is_print_detail_exception=True):
"""
:param queue_name:
:param consuming_function: 处理消息的函数，函数有且只能有一个参数，参数表示消息。是为了简单，放弃策略和模板来强制参数。
:param threads_num:
:param max_retry_times:
:param log_level:
:param is_print_detail_exception:
"""
self._queue_name = queue_name
self.consuming_function = consuming_function
self.threadpool = BoundedThreadPoolExecutor(threads_num)
self._max_retry_times = max_retry_times
self.logger.setLevel(log_level * 10)
self.logger.info(f'{self.__class__} 被实例化')
self._is_print_detail_exception = is_print_detail_exception
self.rabbitmq_helper = RabbitMqHelper(heartbeat_interval=30)
channel = self.rabbitmq_helper.creat_a_channel()
channel.queue_declare(queue=self._queue_name, durable=True)
channel.basic_qos(prefetch_count=threads_num)
self.channel = channel
LogManager('pika.heartbeat').get_logger_and_add_handlers(1) @decorators.keep_circulating(1) # 是为了保证无论rabbitmq异常中断多久，无需重启程序就能保证恢复后，程序正常。
def start_consuming_message(self):
def callback(ch, method, properties, body):
msg = body.decode()
self.logger.debug(f'从rabbitmq取出的消息是： {msg}')
# ch.basic_ack(delivery_tag=method.delivery_tag)
self.threadpool.submit(self.__consuming_function, ch, method, properties, msg) self.channel.basic_consume(callback,
queue=self._queue_name,
# no_ack=True
)
self.channel.start_consuming() @staticmethod
def ack_message(channelx, delivery_tagx):
"""Note that `channel` must be the same pika channel instance via which
the message being ACKed was retrieved (AMQP protocol constraint).
"""
if channelx.is_open:
channelx.basic_ack(delivery_tagx)
else:
# Channel is already closed, so we can't ACK this message;
# log and/or do something that makes sense for your app in this case.
pass def __consuming_function(self, ch, method, properties, msg, current_retry_times=0):
if current_retry_times < self._max_retry_times:
# noinspection PyBroadException
try:
self.consuming_function(msg)
# ch.basic_ack(delivery_tag=method.delivery_tag)
self.rabbitmq_helper.connection.add_callback_threadsafe(functools.partial(self.ack_message, ch, method.delivery_tag))
except Exception as e:
self.logger.error(f'函数 {self.consuming_function} 第{current_retry_times+1}次发生错误，\n 原因是{e}', exc_info=self._is_print_detail_exception)
self.__consuming_function(ch, method, properties, msg, current_retry_times + 1)
else:
self.logger.critical(f'达到最大重试次数 {self._max_retry_times} 后,仍然失败')
# ch.basic_ack(delivery_tag=method.delivery_tag)
self.rabbitmq_helper.connection.add_callback_threadsafe(functools.partial(self.ack_message, ch, method.delivery_tag)) if __name__ == '__main__':
rabbitmq_publisher = RabbitmqPublisher('queue_test')
[rabbitmq_publisher.publish(str(i)) for i in range(1000)] def f(msg):
print('.... ', msg)
time.sleep(10) # 模拟做某事需要10秒种。 rabbitmq_consumer = RabbitmqConsumer('queue_test', consuming_function=f, threads_num=20)
rabbitmq_consumer.start_consuming_message()
1、放入任务 （图片鼠标右键点击新标签打开查看原图）
/2、
2、开启消费者，写一个函数传给消费者类。
3、并发运行效果。
rabbitmq这个专业的消息中间件就是比redis作为消息中间件专业了很多。
