rabbitMQ basicQos和basicAck作用和关系

RabbitMQ高级应用(三)消费端限流策略(basicQos) 高并发情况下,队列里面一瞬间就就积累了上万条数据,但是消费者无法同时处理这么多请求,这个时候当我们打开客户端,瞬间就有巨量的信息给推送过来、但是客户端是没有办法同时处理这么多数据的,结果就是消费者(客户端)挂掉了.... 这种场景下我们就需要对消费端进行限流 关键代码:channel.basicQos(); 阅读详情
import com.rabbitmq.client.Channel;
import com.rabbitmq.client.Connection;
import com.bs.utils.ConnectionUtil;


public class Producer {
    private final static String EXCHANGE_NAME = "direct_exchange";

    public static void main(String[] args) throws Exception {
        //1、获取连接
        Connection connection = ConnectionUtil.getConnection("localhost", 5672, "/", "guest", "guest");
        //2、声明信道
        Channel channel = connection.createChannel();
        //3、声明交换器,类型为direct
        channel.exchangeDeclare(EXCHANGE_NAME, "direct");
        //4、创建消息
        String message = "hello rabbitmq";
        //5、发布消息
        channel.basicPublish(EXCHANGE_NAME, "add", null, message.getBytes());
        System.out.println("生产者发送" + message + "'");
        //6、关闭通道
        channel.close();
        //7、关闭连接
        connection.close();
    }
}
消费者
import com.rabbitmq.client.Channel;
import com.rabbitmq.client.Connection;
import com.rabbitmq.client.QueueingConsumer;
import com.bs.utils.ConnectionUtil;

public class Consumer1 {

    private final static String QUEUE_NAME = "direct_queue_1";

    private final static String EXCHANGE_NAME = "direct_exchange";

    public static void main(String[] args) throws Exception{
        //1、获取连接
        Connection connection = ConnectionUtil.getConnection("localhost",5672,"/","guest","guest");
        //2、声明通道
        Channel channel = connection.createChannel();
        //3、声明队列
        channel.queueDeclare(QUEUE_NAME, false, false, false, null);
        //4、绑定队列到交换机,指定路由key为update
        channel.queueBind(QUEUE_NAME,EXCHANGE_NAME,"update");
        channel.queueBind(QUEUE_NAME,EXCHANGE_NAME,"delete");
        channel.queueBind(QUEUE_NAME,EXCHANGE_NAME,"add");
        //同一时刻服务器只会发送一条消息给消费者
        channel.basicQos(1);
        //5、定义队列的消费者
        QueueingConsumer queueingConsumer = new QueueingConsumer(channel);
        //6、监听队列,手动返回完成状态
        channel.basicConsume(QUEUE_NAME,false,queueingConsumer);
        //6、获取消息
        while (true){
            QueueingConsumer.Delivery delivery = queueingConsumer.nextDelivery();
            String message = new String(delivery.getBody());
            System.out.println(" 消费者1:" + message + "'");
            //消费者1接收一条消息后休眠10毫秒
            Thread.sleep(10);
            //返回确认状态
            channel.basicAck(delivery.getEnvelope().getDeliveryTag(),false);
        }
    }

}

 

RabbitMQ中的概念,channel.basicQos(1)指该消费者在接收到队列里的消息但没有返回确认结果之前,队列不会将新的消息分发给该消费者。队列中没有被消费的消息不会被删除,还是存在于队列中。

Prefetch count 和 basicQos中的参数一致(Prefetch count就是basicQos的参数值)

channel.basicQos(1);和channel.basicAck(delivery.getEnvelope().getDeliveryTag(),false);是配套使用,只有在channel.basicQos被使用的时候channel.basicAck(delivery.getEnvelope().getDeliveryTag(),false)才起到作用。
9-ACK消费者消息确认机制 待办 阅读详情

相关推荐

rabbitmq channel接口常用方法详解

Queue.DeclareOk queueDeclare(String queue, boolean durable, boolean exclusive, boolean autoDelete, Map<String, Object> arguments) throws IOException;解释: 方法作用: 声明一个队列

浪丶荡 9803

手动的进行消息应答

文章目录消息应答概念消息应答的方法"如果手动进行消息应答RabbitMQ中的消息不会丢失"的测试思路生产者代码消费者代码测试启动生产者启动消费者生产者生产两个消息看看消费者的接收情况 消息应答 概念 现在有很多消费者线程同时访问RabbitMQ消息队列,如果其中的一个消费者取到RabbitMQ消息队列的一个消息之后,突然挂掉了,那么RabbitMQ消息队列中的这条消息是不是就丢失了呢?我们该怎么解决这个问题呢? 我们引入了消息应答机制,当消费者从RabbitMQ消息队列中取出消息并且成功的处理了之后,消费者

xzy的博客 5329

从零实现反激式电源仿真:LTspice高级应用实例

深入解析反激式开关电源的建模与仿真过程,利用LTspice实现从零搭建电路,精准验证电路仿真效果,提升电源设计效率与可靠性。

weixin_36149538的博客 1897

RabbitMQ消息可靠性解决方案

1. 解决方案 客户端代码中的异常捕获,包括生产者和消费者 AMQP/RabbitMQ的事务机制 发送端确认机制 消息持久化机制 Broker端的高可用集群 消费者确认机制 消费端限流 消息幂等性 2. 异常捕获机制 先执行业务操作,业务操作成功后执行消息发送,消息发送过程通过try catch 方式捕获异常, 在异常处理理的代码块中执行行回滚业务操作或者执行行重发操作等。这是一种最大努力确保的方式, 并无法保证100%绝对可靠,因为这里没有异常并不代表消息就一定投递成功。 boolean result

rzpy_qifengxiaoyue的博客 1195

UE5抗锯齿实战:如何用TAA解决闪烁问题(附参数优化指南)

本文深入探讨了UE5中TAA(时间性抗锯齿)技术如何有效解决画面闪烁问题,并提供了详细的参数优化指南。通过基础参数配置、高级优化技巧以及材质层面的策略,帮助开发者显著提升视觉质量。文章还介绍了诊断工具链,助力快速定位并解决顽固闪烁问题。

solidity8miner的博客 428

ali140滑块

ali 140滑块采用补环境的方式进行逆向,需要的文件主要为这两个,其中collina.js是最主要的环境校验和参数加密的逻辑,这两个文件放本地进行补环境。然后挂上代理,先保证能够正常运行代码,补了些许后能够正常运行了,我们打印最主要的加密函数看看:n值就是得到:挂上代理看看鼠标事件,不管加密逻辑,关心入参即可:收集一些鼠标事件传进记录的函数。其余就是处理一些环境值,一些resetSA的逻辑就行。截图记录:

bboysteed的博客 1678

rabbitmq设置channel.basicQos无效

rabbitmq设置channel.basicQos无效 解决方法: 在消费者端配置channel.basicQos(1,false)后,还是瞬间把队列里的消息一下子就全部获取完了,但我客户端代码处理业务没那么快,导致消费者端的服务器资源被耗尽。 原来消费者端要把自动确认autoAck设置为false,basicQos才有效果。 比如:http://www.yayihouse.com...

qq_30908729的博客 4472

【自动驾驶学习笔记——Prescan和Simulink 学习笔记和问题记录(1)】

主要记录一些Prescan/Simulink 联合仿真出现的问题。

ly1336545868的博客 1465

rabbitmq channel参数详解

参考https://blog.youkuaiyun.com/qq_26286487/article/details/81004694 rabbitmq channel参数详解 1、Channel 1.1 channel.exchangeDeclare(): type:有direct、fanout、topic三种 durable:true、false true:服务器重启会保留下来Exchange。警告:仅设置...

weixin_40648117的博客 528

opencv基础57-模板匹配cv2.matchTemplate()->(目标检测、图像识别、特征提取)

OpenCV 提供了模板匹配(Template Matching)的功能,它允许你在图像中寻找特定模板(小图像)在目标图像中的匹配位置。模板匹配在计算机视觉中用于目标检测、图像识别、特征提取等领域。: 首先,加载目标图像和要匹配的模板图像。: 选择适当的匹配方法,例如 cv2.TM_CCOEFF、cv2.TM_CCOEFF_NORMED、cv2.TM_CCORR、cv2.TM_CCORR_NORMED、cv2.TM_SQDIFF 或 cv2.TM_SQDIFF_NORMED。每种方法对应不同的匹配计算方式。

小海聊智造 1万+

STMicroelectronics 系列:STM32G4 系列_(11).STM32G4系列编程基础

通过本文,我们学习了如何在 STM32CubeIDE 中搭建开发环境,创建新项目,配置 GPIO、定时器和 ADC 外设,并编写相应的代码示例。这些基础知识将帮助你快速上手 STM32G4 系列的编程。希望本文对你有所帮助,祝你编程愉快!

2401_87715305的博客 1279

RabbitMQ - 自动确认与手动确认的优劣对比及适用场景

本文对比分析了RabbitMQ的两种消息确认模式:自动确认和手动确认。自动确认模式性能高但可靠性低,适合处理非关键性消息;手动确认模式可靠性强但性能较低,适合关键业务场景。文章通过Java代码示例展示了两种模式的实现方式,并详细讨论了它们的适用场景、性能特征和可靠性保障机制,帮助开发者根据业务需求选择最合适的确认策略。

千淘万漉虽辛苦,吹尽狂沙始到金 2万+

AUTOSAR模式管理-EcuM多核处理及其他概念

因此,EcuM不请求状态。因此,BswM必须确保,例如,在调用EcuM_GoHalt或EcuM_GoPoll之前,必须将关机目标从其默认值更改为ECUM_STATE_SLEEP。对于ECU的关闭,BswM(位于主EcuM的同一个分区中)最终调用主核上的GoOff,该GoOff将请求分发给所有的从核。在ECU Manager模块的早期版本中,睡眠目标被特殊处理,因为在ECU中实现的睡眠模式取决于ECU的功能。在使用了外部的SBC控制相关的MCU的下电的时候,在OFF阶段最后调用SBC进入到Sleep的指令。

m0_49148264的博客 1340

RabbitMQ 手动签收

下面这基础地方都必须设置,不然无效 // 同一时刻服务器只会发一条消息给消费者 channel.basicQos(1); // 消息的标识,false只确认当前一个消息收到,true确认所有consumer获得的消息 channel.basicAck(message.getMessageProperties().getDeliveryTag(), false); ...

Gblfy_Blog 1681

Python量化交易:主流回测框架Backtrader入门详解

Python量化交易框架Backtrader入门指南 Backtrader是一个功能强大的Python量化回测框架,可以帮助交易者专注于策略逻辑而非基础设施。本文介绍了Backtrader的核心架构和使用方法: 框架优势:自动处理订单执行、手续费、滑点、仓位管理等复杂问题,采用事件驱动模式接近真实交易场景。 核心组件: Cerebro:控制中心 Strategy:交易策略 Data Feed:数据输入 Broker:模拟经纪商 Indicators:技术指标 使用流程: 安装:pip install bac

Science prince的博客 935

LinePhone3.2.7(android端最新版本) sip客户端

SIP客户端For android:LinePhone3.2.7(最新版本),官网地址:http://www.linphone.org/

RabbitMq基础篇-09-channel接口常用几种参数详解

为了保证消息从队列可靠地达到消费者, RabbitMQ 提供了消息确认机制( message acknowledgement), 消费者在订阅队列时,可以指定autoAck参数,当autoAck 等于false时,RabbitMQ会等待消费者显式地回复确认信号后才从内存(或者磁盘)中移去消息(实质上是先打上删除标记,之后再删除)。当autoAck 等于true时,RabbitMQ 会自动把发送出去的消息置为确认, 然后从内存(或者磁盘)中删除,而不管消费者是否真正地消费到了这些消息。

Alan0517 1万+

深度学习工具包 DeepLearningToolbox

深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

channel rabbitmq 配置_RabbitMQ (Channel参数篇)

Channel1:方法ExchangeDeclarevoid ExchangeDeclare(string exchange, string type, bool durable, bool autoDelete, IDictionary arguments);参数:1.1:exchange:交换机的名称1.2:Type:交换机的类型(direct,fanout,topic)1.3:durable...

weixin_36269141的博客 2083

C# WinForm 基于DirectShow.dll编写的程序,可边预览边保存视频,音频和图片

基于微软的DirectShow.dll的二次开发1.自动识别连接的摄像头跟麦克风且可以对其进行选择(默认选中第一个)2.可录制wmv和avi两种格式的视频并且都带有音频,avi可以录制的分辨率和帧数进行选择,wmv默认进行了淸晰度优化如不喜欢可去除代码中有相关提示3.可在预览点开之后进行拍照,也可边录制视频边拍照4.添加了视频和音频的压缩功能(酌情使用)这次二次开发发现C#对DirectShow的相关信息太少,这次做完也不敢藏私共享出来,还望大家指正

Data-Structures-and-Algorithm-Analysis-in-Java.pdf

作者: Weiss, Mark A. 出版社: Addison Wesley Data Structures and Algorithm Analysis in Java is an “advanced algorithms” book that fits between traditional CS2 and Algorithms Analysis courses. In the old ACM Curriculum Guidelines, this course was known as CS7. This text is for readers who want to learn good programming and algorithm analysis skills simultaneously so that they can develop such programs with the maximum amo...

RabbitMQ安装及配置

官网 http://www.rabbitmq.com/install-debian.html#apt 下载: sudo apt-get install rabbitmq-server 启停: service rabbitmq-server start service rabbitmq-server stop 启用控制台 rabbitmq-plugins enabl...

lu1112的博客 288

RabbitMQ的Java入门示例代码

本文介绍了使用原生 Java 客户端(amqp-client)实现 RabbitMQ 的两种基本模式:简单模式和工作队列模式。代码示例分为连接工具类、简单模式(单生产者-单消费者)和工作队列模式(单生产者-多消费者)三部分。 核心要点: 通过 RabbitMQUtils 封装连接创建逻辑 简单模式演示了基础的消息发送/接收流程 工作队列模式展示了多消费者负载均衡场景,通过 basicQos(1) 实现公平分发 消费者采用 DeliverCallback 异步接收消息 包含队列声明、消息发布、消费者回调等关键

ywl470812087的博客 208

RabbitMQ消息确认机制在大数据场景下的优化实践

消息队列作为分布式系统的核心组件,其可靠性直接影响业务数据的完整性。RabbitMQ通过ACK机制实现消息的可靠投递,其中手动确认模式(autoAck=false)能确保消息不丢失,而basicNack和basicReject提供了更精细的流控能力。在高并发场景下,通过批量确认和prefetch调优可显著提升吞吐量,配合幂等设计和集群化部署,能够满足金融级系统对消息可靠性的严苛要求。本文结合电商订单和实时交易等场景,详解如何通过RabbitMQ消息确认机制应对PB级数据挑战。

weixin_34162228的博客 335

RabbitMQ 面试题完整答案

Java 后端面试高频,可直接背诵

专注 Java 后端开发,分享基础语法、JVM、并发、Spring 全家桶、微服务、分布式、性能优化等实战干货。坚持输出高质量技术笔记,用简单语言讲清复杂原理,助力每一位 Java 开发者稳步进阶。 357

rabbitmq.channel参数详解

rabbitmq.channel参数详解 channel.exchangeDeclare(): 1、type:有direct、fanout、topic三种 2、durable:true、false true:服务器重启会保留下来Exchange。警告:仅设置此选项,不代表消息持久化。即不保证重启后消息还在 3、autoDelete:true、false.true:当已经没有消费者时,服务器是否可以删除该Exchange Exchange.DeclareOk exchangeDeclare(String e

拒绝熬夜啊的博客 1964

rabbitmq之工作队列(一)

文章目录概念1.轮训分发消息1.1代码举例1.2观察结果2.消息应答2.1自动消息应答2.1.1 优缺点2.2手动消息应答2.2.1 优缺点2.2.2 手动消息应答的方法2.2.3 Multiple 的解释2.3消息重新入队列2.4手动消息应答代码测试2.4.1观察结果3.持久化3.1队列持久化3.2消息持久化4.不公平分发5.预取值6.确认发布 概念 工作队列(又称任务队列)的主要思想是避免立即执行资源密集型任务,而不得不等待它完成。相反我们安排任务在之后执行。我们把任务封装为消息并将其发送到队列。在后台

旅人的博客 708

rabbitmq.channel方法介绍

rabbitmq channel

Java_ESS的博客 470
上一篇: 负载均衡 ribbon
下一篇: ajax 调用SpringBoot中的restful接口 两个ajax分别请求两个不同的接口,要使两个接口中的session是同一个(session共享)
rabbit0708
博客等级 码龄15年 12粉丝 27原创
评论 1
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符
 
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

个

红包个数最小为10个

元

红包金额最低5元

当前余额3.43元 前往充值 >
需支付:10.00元
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付元
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值