微服务核心模式：一消息两服务

Micro
Archit
需积分: 7 2 下载量 133 浏览量 更新于2024-07-17 收藏 17.18MB PDF 举报
"《The Tao of Microservices》第一版，由Manning出版社于2017年12月发行，提供了英文PDF版本，适合阅读和做笔记。此书已去除只读模式，并重新安排了页码，便于在不同服务之间进行跳转。标签涉及微服务架构、扩展性和设计原则。书中部分内容探讨了核心消息模式，包括一对一消息服务和一对多消息服务。" 《The Tao of Microservices》是关于微服务架构的一本深入指南，旨在帮助读者理解和实践微服务设计的核心原则。书中的核心消息模式是理解微服务间通信的关键。 首先，一对一消息服务（Synchronous/Consumed）模式类似于HTTP请求/响应模型。在这种模式下，发送方微服务期望立即收到响应，且消息被消费，意味着只有一个接收方微服务会生成响应。这种通信方式确保了点对点的同步交互。例如，当微服务A发送一个命令消息到微服务B时，它期待至少有一个即时响应，而微服务B则处理这个命令并返回结果。 其次，一对多消息服务（Synchronous/Observed）模式中，消息是被观察的，而不是被消耗。这意味着不是所有接收者都会生成响应，而是其中一部分。这种模式通常用于需要多个微服务协作的情况，如生成多个推荐。例如，微服务A可能发布一个请求，多个微服务B可以观察到该请求并各自生成一个推荐响应。消息的分发机制由消息层抽象处理，以便解耦具体实现。 书中通过具体的例子和图示，如微服务A和微服务B之间的实线表示同步通信，空箭头表示被观察等，清晰地展示了这些模式如何在实际微服务架构中运作。这些模式对于理解微服务间的协作和数据流至关重要，有助于设计出可扩展、高可用的微服务系统。作者通过深入的分析和案例，为读者提供了构建微服务架构的实用指导和理论依据。

The+Tao+of+Microservices-Manning(2017).pdf

2017-12-24 上传
The Tao of Microservices guides you on the path to understanding how to apply microservice architectures to your own real-world projects. This high-level book offers a conceptual view of microservice ...

The Tao of Microservices

2018-09-16 上传
The Tao of Microservices Authors: Richard Rodger ISBN 10: 1617293148 ISBN 13: 9781617293146 Edition: 1 Released: 2017-12-31 Format: Paperback (328 pages) List Price: $49.99 ...

clc; clear; close all; warning off; data=load('PDWdata.txt'); t=data(:,4); ysort = sort(t); N = length(ysort); K = 15000; Tao1 = 0; Tao2 = 1200; b =(Tao2-Tao1)/K; Cv = zeros(1,K); Dv = zeros(1,K); for i=1:K Tao_s(i)=(i-1/2)*(Tao2-Tao1)/K+Tao1; end n = 2; while n<=N m=n-1; while m>=1 Tao = ysort(n)-ysort(m); if Tao>Tao1 & Tao<=Tao2 for k=1:K if Tao>(Tao_s(k)-b/2) & Tao<=(Tao_s(k)+b/2) Dv(k) = Dv(k)+exp(2*pi*ysort(n)*j/Tao); Cv(k) = Cv(k)+1; end end end if Tao>Tao2 m = m - 1; break; end if Tao<=Tao1 n = n + 1; break; end m = m - 1; end n = n + 1; end [dd,index]=sort(Dv,'descend');%降序排列 PRI=Tao_s(index(1:3));%确定前3个为有效的PRI值 PRI=round(PRI)%取整 figure; plot(Tao_s,abs(Dv),'b-*') ; hold on plot(Tao_s,0.3*11765./Tao_s,'r-') xlabel('PRI/us'); ylabel('PRI累积统计值') title('PRI变换法'); ylim([0,160]);

2023-06-03 上传
Tao_s(i) = (i - 1 / 2) * (Tao2 - Tao1) / K + Tao1; end n = 2; while n m = n - 1; while m >= 1 Tao = ysort(n) - ysort(m); if Tao > Tao1 & Tao <= Tao2 for k = 1:K if Tao > (Tao_s(k) - b / 2) & ...

var x pi tao a r y i; varexo e_tao e_a e_new; %========================================================================== parameters BTA SI_X SI_PI RHO LAM FAI THE SIG_A SIG_TAO ALA; BTA = 0.97; SI_X = 0; SI_PI = 1.5; RHO = 0.2; LAM = 0.5; FAI = 1; THE = 0.75; SIG_A = 0.02; SIG_TAO = 0.02; ALA = (1-THE)*(1-BTA*THE)/THE; %========================================================================== model; %x = x(+1) - i + pi(+1) + (1-LAM)/(FAI+1)*tao - (1-RHO)*a; x = x(+1) - SI_X*x - SI_PI*pi + pi(+1) + (1-LAM)/(FAI+1)*tao - (1-RHO)*a; pi = BTA*pi(+1) + ALA*(FAI+1)*x; tao = LAM*tao(-1) + e_tao; a = RHO*a(-1) + e_a + e_new(-1); y = a - 1/(1+FAI)*tao; r = y(+1) - y + pi(+1); i = r + SI_X*x + SI_PI*pi; end; % the # of equations must be the same as the # of variables %========================================================================== initval; x = 0; pi = 0; tao = 0; a = 0; i = 0; y = 0; r = 0; end; %========================================================================== shocks; var e_new; stderr 0.02; end; %========================================================================== stoch_simul(periods=500,irf=20,order=1);

2023-05-30 上传
该模型包括多个变量，如 x、pi、tao、a、y 和 r，以及多个参数，如 BTA、SI_X、SI_PI、RHO、LAM、FAI、THE、SIG_A、SIG_TAO 和 ALA。其中，model 部分定义了模型的方程式，initval 部分定义了变量的初始值，shocks ...

% eta数据 % 输入:24个激光干涉数据 % 输出:6列eta数据 function [eta,etatstart,etaNpoints]=etadata(t0,sci,scib,tao,epsilon) global Fs M m1 m2 m3 m4 m5 m6; Npoints=length(sci(:,1)); prePoints=10*Fs+2*M; endPoints=2*M; etaNpoints=Npoints-prePoints-endPoints; etatstart=t0+prePoints/Fs; eta=zeros(etaNpoints,12); epsilon1=epsilon(:,1);epsilon2=epsilon(:,2);epsilon3=epsilon(:,3); epsilon4=epsilon(:,4);epsilon5=epsilon(:,5);epsilon6=epsilon(:,6); tao1=tao(:,1);tao2=tao(:,2);tao3=tao(:,3); tao4=tao(:,4);tao5=tao(:,5);tao6=tao(:,6); for ii=prePoints+1:prePoints+etaNpoints ti=t0+(ii-1)/Fs; Ltime=lasertravelTime(ti,0); delay0=Ltime(1:6)*Fs; delay0M=ceil(delay0); delay0e=delay0M-delay0; doppler0=1-Ltime(7:12); d3epsilon5tao5=epsilon5(ii-delay0M(3)+M:-1:ii-delay0M(3)-M+1)-tao5(ii-delay0M(3)+M:-1:ii-delay0M(3)-M+1); d1epsilon6tao6=epsilon6(ii-delay0M(1)+M:-1:ii-delay0M(1)-M+1)-tao6(ii-delay0M(1)+M:-1:ii-delay0M(1)-M+1); d2epsilon4tao4=epsilon4(ii-delay0M(2)+M:-1:ii-delay0M(2)-M+1)-tao4(ii-delay0M(2)+M:-1:ii-delay0M(2)-M+1); d5epsilon3tao3=epsilon3(ii-delay0M(5)+M:-1:ii-delay0M(5)-M+1)-tao3(ii-delay0M(5)+M:-1:ii-delay0M(5)-M+1); d6epsilon1tao1=epsilon1(ii-delay0M(6)+M:-1:ii-delay0M(6)-M+1)-tao1(ii-delay0M(6)+M:-1:ii-delay0M(6)-M+1); d4epsilon2tao2=epsilon2(ii-delay0M(4)+M:-1:ii-delay0M(4)-M+1)-tao2(ii-delay0M(4)+M:-1:ii-delay0M(4)-M+1); d3tao2tao5=tao2(ii-delay0M(3)+M:-1:ii-delay0M(3)-M+1)-tao5(ii-delay0M(3)+M:-1:ii-delay0M(3)-M+1); d1tao3tao6=tao3(ii-delay0M(1)+M:-1:ii-delay0M(1)-M+1)-tao6(ii-delay0M(1)+M:-1:ii-delay0M(1)-M+1); d2tao1tao4=tao1(ii-delay0M(2)+M:-1:ii-delay0M(2)-M+1)-tao4(ii-delay0M(2)+M:-1:ii-delay0M(2)-M+1); eta(ii-prePoints,1)=sci(ii,1)-(epsilon(ii,1)-tao(ii,1))/2-doppler0(3)*fracdelay(delay0e(3))*(d3epsilon5tao5+ d3tao2tao5)/2; eta(ii-prePoints,2)=sci(ii,2)-(epsilon(ii,2)-tao(ii,2))/2-doppler0(1)*fracdelay(delay0e(1))*(d1epsilon6tao6+ d1tao3tao6)/2; eta(ii-prePoints,3)=sci(ii,3)-(epsilon(ii,3)-tao(ii,3))/2-doppler0(2)*fracdelay(delay0e(2))*(d2epsilon4tao4+ d2tao1tao4)/2; eta(ii-prePoints,4)=sci(ii,4)-(epsilon(ii,4)-tao(ii,4))/2-doppler0(5)*fracdelay(delay0e(5))*d5epsilon3tao3/2+(tao(ii,1)-tao(ii,4))/2; eta(ii-prePoints,5)=sci(ii,5)-(epsilon(ii,5)-tao(ii,5))/2-doppler0(6)*fracdelay(delay0e(6))*d6epsilon1tao1/2+(tao(ii,2)-tao(ii,5))/2; eta(ii-prePoints,6)=sci(ii,6)-(epsilon(ii,6)-tao(ii,6))/2-doppler0(4)*fracdelay(delay0e(4))*d4epsilon2tao2/2+(tao(ii,3)-tao(ii,6))/2; eta(ii-prePoints,7)=(scib(ii,1)-sci(ii,1))/m5; eta(ii-prePoints,8)=(scib(ii,2)-sci(ii,2))/m6; eta(ii-prePoints,9)=(scib(ii,3)-sci(ii,3))/m4; eta(ii-prePoints,10)=(scib(ii,4)-sci(ii,4))/m3; eta(ii-prePoints,11)=(scib(ii,5)-sci(ii,5))/m1; eta(ii-prePoints,12)=(scib(ii,6)-sci(ii,6))/m2; end end

2023-05-25 上传
然后，将输入数据中的 epsilon 和 tao 按列分别赋值给对应的变量 epsilon1~6 和 tao1~6。 接下来，通过循环遍历每个需要计算的 eta 数据点，计算出该点对应的激光传输时间 Ltime 和延迟 delay0，以及对应的多普勒...

using Tao.OpenGL.dll

2023-04-07 上传
1. Declare the DLL functions that you want to use in your program using the DllImport attribute. For example: [DllImport("Tao.OpenGL.dll")] public static extern void glClear(int mask); 2. Call the ...

CMake Error: The source "/home/tao/Desktop/xyl/astra_camera_ws/src/CMakeLists.txt" does not match the source "/home/tao/Desktop/xyl/astra_camera_ws/src/ros_astra_camera/CMakeLists.txt" used to generate cache. Re-run cmake with a different source directory. Makefile:1416: recipe for target 'cmake_check_build_system' failed make: *** [cmake_check_build_system] Error 1

2023-07-12 上传
1. 删除之前生成的构建文件夹（如果存在），执行以下命令： ``` rm -rf /home/tao/Desktop/xyl/astra_camera_ws/build ``` 2. 创建一个新的构建文件夹并进入该目录： ``` mkdir /home/tao/Desktop/xyl/astra_...

交叉编译aec+tao

2023-03-29 上传
而TAO（The ACE ORB）是一个轻量级的CORBA实现，可以用于分布式系统中，提供通信、组件管理、消息传递等功能。 交叉编译AEC TAO可以参考以下步骤： 1. 准备交叉编译工具链，根据目标平台的CPU架构和操作系统选择...

将下面代码写成matlab形式 int runBm3d( const Mat image_noisy, Mat& image_basic, Mat& image_denoised ) { int Height = image_noisy.rows; int Width = image_noisy.cols; int Channels = image_noisy.channels(); vector<Mat> block_noisy;//store the patch vector<int>row_idx;//patch idx along the row direction vector<int>col_idx; GetAllBlock(image_noisy, Width, Height, Channels, kHard, pHard, block_noisy, row_idx, col_idx); int bn_r = row_idx.size(); int bn_c = col_idx.size(); tran2d(block_noisy, kHard); vector<int> sim_num;//index number for the selected similar patch in the block vector vector<int> sim_idx_row;//index number for the selected similar patch in the original Mat vector<int> sim_idx_col; vector<Mat>data;//store the data during transforming and shrinking Mat kaiser = gen_kaiser(beta, kHard);//2-D kaiser window float weight_hd = 1.0;//weights used for current relevent patch Mat denominator_hd(image_noisy.size(), CV_32FC1, Scalar::all(0)); Mat numerator_hd(image_noisy.size(), CV_32FC1, Scalar::all(0)); for (int i = 0; i < bn_r; i++) { for (int j = 0; j < bn_c; j++) { //for each pack in the block sim_num.clear(); sim_idx_row.clear(); sim_idx_col.clear(); data.clear(); getSimilarPatch(block_noisy, data, sim_num, i, j, bn_r, bn_c, int((nHard - kHard) / pHard) + 1, NHard, tao_hard);//block matching for (int k = 0; k < sim_num.size(); k++)//calculate idx in the left-top corner { sim_idx_row.push_back(row_idx[sim_num[k] / bn_c]); sim_idx_col.push_back(col_idx[sim_num[k] % bn_c]); } tran1d(data, kHard);//3-D transforming DetectZero(data, lambda3d * sigma);//shrink the cofficient weight_hd = calculate_weight_hd(data, sigma); Inver3Dtrans(data,kHard);//3-D inverse transforming aggregation(numerator_hd, denominator_hd, sim_idx_row, sim_idx_col, data, weight_hd, kHard, kaiser);//aggregation using weigths } } image_basic = numerator_hd / denominator_hd;

2023-05-24 上传
[data,sim_num] = getSimilarPatch(block_noisy, i, j, bn_r, bn_c, int32((nHard - kHard) / pHard) + 1, NHard, tao_hard); %block matching for k = 1:length(sim_num) %calculate idx in the left-top ...

翻译解释一下这段代码clc; clear; close all; warning off; data=load('PDWdata.txt'); t=data(:,4); ysort = sort(t); N = length(ysort); K = 15000; Taomin = 0; Taomax = 1200; b =(Taomax-Taomin)/K; Cv = zeros(1,K); Dv = zeros(1,K); for i=1:K Tao_s(i)=(i-1/2)*(Taomax-Taomin)/K+Taomin; end n = 2; while n<=N m=n-1; while m>=1 Tao = ysort(n)-ysort(m); if Tao>Taomin & Tao<=Taomax for k=1:K if Tao>(Tao_s(k)-b/2) & Tao<=(Tao_s(k)+b/2) Dv(k) = Dv(k)+exp(2*pi*ysort(n)*j/Tao); Cv(k) = Cv(k)+1; end end end if Tao>Taomax m = m - 1; break; end if Tao<=Taomin n = n + 1; break; end m = m - 1; end n = n + 1; end [dd,index]=sort(Dv,'descend');%降序排列 PRI=Tao_s(index(1:3));%确定前3个为有效的PRI值 PRI=round(PRI)%取整 figure; plot(Tao_s,abs(Dv),'b-*') ; hold on plot(Tao_s,0.3*11765./Tao_s,'r-') xlabel('PRI/us'); ylabel('PRI累积统计值') title('PRI变换法'); ylim([0,160]);

2023-06-05 上传
1. 清空MATLAB工作环境，包括清除命令窗口、关闭所有图形窗口、清空工作区变量。 2. 从名为 "PDWdata.txt" 的文件中读取数据，并将第四列数据存储在一个名为 "t" 的变量中。 3. 对 "t" 变量进行排序，存储在 ...
retry
  • 粉丝: 0
  • 资源: 4
上传资源 快速赚钱

最新资源