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+    <meta itemprop="headline" content="秒杀开发历程(三)">
+    <meta itemprop="description" 
content="介绍如何一步一步构建秒杀项目的过程">
+    <meta itemprop="datePublished" content="September 07, 2017">
+    <meta itemprop="dateModified" content="September 13, 2017">
+
+    <div class="page__inner-wrap">
+      
+        
+          <header>
+            <h1 class="page__title" 
itemprop="headline">秒杀开发历程(三)
+</h1>
+            
+              <p class="page__meta"><i class="fa fa-clock-o" 
aria-hidden="true"></i> 
+
+
+
+
+  少于 1 分钟 阅读
+
+</p>
+            
+          </header>
+        
+      
+
+      <section class="page__content" itemprop="text">
+        <p>  <a 
href="/cn/docs/seckill-development-journey-part-II/">上文</a> 
中我们已经构建了一个完整功能的秒杀系统,版本定为0.1.0-RELEASE;您应该发现,数据的持ä¹
…
化集中在单一的数据库上,秒杀作为一个高并发压力的场景,这种架构很难适应可扩展的要求,并且正如我们一开始就提到的,微服务推荐拥有独立的存储,所以本篇开始我们将é
…åˆEvent 
Sourcing实现CQRS模式,以增强应对大规模并发压力的能力。</p>
+
+<h2 id="cqrs">CQRS</h2>
+<p>  <a 
href="https://docs.microsoft.com/en-us/azure/architecture/patterns/cqrs";>CQRS</a>
 的含义是命令查询职责分离(Command Query Responsibility 
Segregation),是一种常用的程序设计模式,常常与Event 
Sourcing一起使用,关于CQRS有一张科普的图,来源于Microsoft 
MSDN:</p>
+
+<p><img src="/assets/images/seckill-develop-course-part-III-cqrs.png" 
alt="图1 CQRS" class="align-center" /></p>
+
+<h2 id="改进设计">改进设计</h2>
+<p>  <a 
href="https://docs.microsoft.com/en-us/azure/architecture/patterns/cqrs";>CQRS</a>是读写分离以及消息驱动的最佳实践,现在我们把目å
…‰èšç„¦åœ¨åŽŸæ¥çš„MySQL数据库上:</p>
+<ol>
+  
<li>数据库应该被拆分为ReadDB和WriteDB,分别承载读密集的Query微服务和写密集的Command微服务;</li>
+  <li>ReadDB和WriteDB之间采用Event 
Message驱动方式完成数据同步,因
此我们还需要引进一个新的微服务,我们将它命名为Event(Processor)微服务;</li>
+  <li>Message 
Broker目前有很多非常棒的方案,例如RabbitMQ、Kafka等,由于我们的秒杀系统基于ServiceComb和Spring
 Boot,为了能够快速构建这个新特性,我们选用内
置的ActiveMQ,它支持内
存队列模式,非常容易构建测试,符合我们敏捷开发的方式。<br
 />
+ 改进完成后的架构如下图:</li>
+</ol>
+
+<p><img src="/assets/images/seckill-develop-course-part-III-arch.png" 
alt="图2 改进架构" class="align-center" /></p>
+
+<h2 id="功能实现">功能实现</h2>
+<h3 id="数据库拆分">数据库拆分</h3>
+<h4 id="writedb">WriteDB</h4>
+<p>  Admin微服务维护Promotion实体;<br />
+  Command微服务写å…
¥Event值对象,当恢复Promotion的时候,Relapy Event。</p>
+<h4 id="readdb">ReadDB</h4>
+<p>  Event微服务写入ActivePromotion值对象和Coupon值对象;<br 
/>
+  Query微服务查询ActivePromotion值对象和Coupon值对象。</p>
+
+<h3 id="command微服务message-publisher组件">Command微服务Message 
Publisher组件</h3>
+<p>  之前的版本由于没有引入Event 
Sourcing,Event实体只需要直接写å…
¥æ•°æ®åº“即可,现在需要将Event发布给Message 
Broker,为了支持云服务(<a 
href="http://www.hwclouds.com/";>华为云</a>)提供的分布式消息服务,我们定义了通用消息发布接口:</p>
+
+<div class="language-java highlighter-rouge"><pre 
class="highlight"><code><span class="kd">public</span> <span 
class="kd">interface</span> <span class="nc">SecKillMessagePublisher</span> 
<span class="o">{</span>
+  <span class="kt">void</span> <span class="nf">publishMessage</span><span 
class="o">(</span><span class="n">String</span> <span 
class="n">messageContent</span><span class="o">);</span>
+<span class="o">}</span>
+</code></pre>
+</div>
+
+<h3 id="event微服务的实现">Event微服务的实现</h3>
+<p>  Event微服务订阅Message Broker获取Event Message,然后æ 
¹æ®ä¸åŒçš„Event类型,转化为对ReadDB中的值对象操作或Coupon值对象操作:</p>
+<ul>
+  <li>Promotion Start Message:创建ActivePromotion值对象;</li>
+  <li>Promotion Finish Message:删
除对应PromotionId的ActivePromotion值对象;</li>
+  <li>Coupon Grabbed Message:创建Coupon值对象;</li>
+</ul>
+
+<p>  同样,为了支持云服务(<a 
href="http://www.hwclouds.com/";>华为云</a>)提供的分布式消息服务,我们定义了通用消息订é˜
…接口:</p>
+
+<div class="language-java highlighter-rouge"><pre 
class="highlight"><code><span class="kd">public</span> <span 
class="kd">interface</span> <span class="nc">SecKillMessageSubscriber</span> 
<span class="o">{</span>
+  <span class="kt">void</span> <span class="nf">subscribeMessage</span><span 
class="o">(</span><span class="n">String</span> <span 
class="n">messageContent</span><span class="o">);</span>
+<span class="o">}</span>
+</code></pre>
+</div>
+
+<h3 id="query微服务的重构">Query微服务的重构</h3>
+<p>  经过了读写分离的改造
,现在所有活跃的Promotion都保存在ActivePromotion值对象表中,而客户成功秒杀的Coupon都保存在Coupon值对象表中,å›
 æ­¤ç›´æŽ¥æŸ¥è¯¢å³å¯ï¼Œä¸å†éœ€è¦Replay Event。</p>
+
+<h2 id="一键启动">一键启动</h2>
+<p>  现在,我们完成了完整的Event Sourcing架构改造
,为了能够方便启动,我们提供了编排好的脚本,使用Docker 
Compose将所有服务一条指令全部拉起来,具体的操作请参见<a 
href="https://github.com/ServiceComb/seckill";>SecKill</a>。</p>
+
+<h3 id="在docker-toolbox中启动的seckill">在Docker 
ToolBox中启动的SecKill</h3>
+
+<p><img 
src="/assets/images/seckill-develop-course-part-III-seckill-all-in-one.png" 
alt="图3 SecKill All-in-One" class="align-center" /></p>
+
+<h2 id="参考资料">参考资料</h2>
+<ol>
+  <li><a 
href="https://docs.microsoft.com/en-us/azure/architecture/patterns/cqrs";>Command
 and Query Responsibility Segregation (CQRS) pattern</a> by Microsoft Azure 
Team</li>
+  <li><a 
href="https://msdn.microsoft.com/en-us/library/jj591559.aspx";>Introducing Event 
Sourcing</a> by Microsoft patterns &amp; practices Developer Center</li>
+  <li><a href="https://martinfowler.com/eaaDev/EventSourcing.html";>Event 
Sourcing</a> by Martin Fowler</li>
+</ol>
+
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+    <meta itemprop="headline" 
content="在Kubernetes集群中使用JMeter对Company示例进行压力测试">
+    <meta itemprop="description" 
content="介绍如何在Kubernetes集群上使用JMeter进行压力测试">
+    <meta itemprop="datePublished" content="August 25, 2017">
+    <meta itemprop="dateModified" content="August 26, 2017">
+
+    <div class="page__inner-wrap">
+      
+        
+          <header>
+            <h1 class="page__title" 
itemprop="headline">在Kubernetes集群中使用JMeter对Company示例进行压力测试
+</h1>
+            
+              <p class="page__meta"><i class="fa fa-clock-o" 
aria-hidden="true"></i> 
+
+
+
+
+  少于 1 分钟 阅读
+
+</p>
+            
+          </header>
+        
+      
+
+      <section class="page__content" itemprop="text">
+        <h2 id="背景">背景</h2>
+
+<p>  压力测试是评估应用性能的一种有效手段。此外,越来越多的应用被拆分为多个微服务而每个微服务的性能不一,有的微服务是计算密集型,有的是IO密集型。</p>
+
+<p>  因
此,压力测试在基于微服务架构的网络应用中扮演着越来越重要的角色。本文将在<a
 href="https://kubernetes.io/";>Kubernetes</a>集群中使用<a 
href="https://www.google.com.hk/url?sa=t&amp;rct=j&amp;q=&amp;esrc=s&amp;source=web&amp;cd=1&amp;ved=0ahUKEwiv9rjg7u_VAhUkxoMKHfoYDaYQFggvMAA&amp;url=http%3A%2F%2Fjmeter.apache.org%2F&amp;usg=AFQjCNHIHCOA-F9LnhaAn_STCWyPPgOpdw";>JMeter
 3.2</a>来对<a 
href="https://github.com/ServiceComb/ServiceComb-Company-WorkShop";>Company应用</a>进行性能评估。</p>
+
+<p>  在上文<a 
href="http://servicecomb.io/cn/docs/autoscale-on-company/";>《微服务化后的按需精细化资源控制》</a>中已了解到Manager服务的资源需求最大,本次使用JMeter对Company应用进行精细的性能测试。</p>
+
+<h2 id="制定jmeter测试方案">制定JMeter测试方案</h2>
+<p>  制定的测试方案为:</p>
+
+<ul>
+  <li>
+    <p>由于登录认证会造成较大时延,故
在压力测试主体运行前完成用户认证事宜;</p>
+  </li>
+  <li>
+    <p>对Company应用多个接口持续并发访问,QueryWorker, 
QueryBeekeeperDrone, 
QueryBeekeeperQueen分别对Company中的服务发起请求压力。</p>
+  </li>
+</ul>
+
+<p>  测试方案文件笔者已托管于github上,可直接获取:</p>
+
+<div class="language-bash highlighter-rouge"><pre class="highlight"><code>git 
clone https://github.com/ServiceComb/ServiceComb-Company-WorkShop
+<span class="nb">cd </span>ServiceComb-Company-WorkShop/stress-tests
+</code></pre>
+</div>
+
+<p class="figure-caption"><img src="/assets/images/company_test_plan.png" 
alt="图1 测试计划" /><br />
+图1 测试计划</p>
+
+<p>  在我们测试计划的开始,我们设置了一些在所有线程组都能å
…±äº«çš„全局配置。其中,<em>CSV Data Set 
Config</em>组件从本地csv文件中加载测试服务器的信息。<em>HTTP 
Request 
Defaults</em>组件则为每个请求预设了默认的请求服务器信息,如IP和端口。<em>User
 Defined Variables</em>组件定义了全局共享的变量。<em>HTTP Header 
Manager</em>组件自动为每个请求添加HTTP的请求头部。</p>
+
+<p>  接着就是<em>setUp</em>线程组了。å…
¶ä½œç”¨ä¸»è¦æ˜¯å¤„理用户认证。由于Cookie在网络应用中得到较广泛的应用,å›
 æ­¤JMeter中内置的认证方式是通过<em>HTTP Cookie 
Manager</em>组件来完成的。然而,我们的Company示例采用的是基于Token的认证方式而不是基于Cookie的认证方式。å›
 æ­¤ï¼Œè¿™ç»™æˆ‘们在JMeter中处理认证添加了一点难度。<em>Remove 
header pre 
processor</em>组件使用了以下的脚本来去掉登录时请求中带有默认的请求头部,即登录请求时并不需要带有鉴权的请求头部。</p>
+<div class="language-shell highlighter-rouge"><pre 
class="highlight"><code>import org.apache.jmeter.protocol.http.control.Header;
+sampler.getHeaderManager<span class="o">()</span>.removeHeaderNamed<span 
class="o">(</span><span class="s2">"Authorization"</span><span 
class="o">)</span>;
+</code></pre>
+</div>
+<p>  然后我们通过<em>Set up 
Login</em>组件发起一次登录请求来获取用户登录鉴权信息。之后再通过正则表达式提取组件<em>authorization_extractor</em>来提取响应头部中的<em>Authorization</em>对应的值。由于变量æ—
 æ³•åœ¨ä¸åŒçš„线程组中共享和传递,这时候<em>BeanShell 
PostProcessor</em>组件就派上用场了,它主要工作就是将当前线程组中的目æ
 ‡å˜é‡è½¬æ¢ä¸ºå…¨å±€å±žæ€§ã€‚</p>
+<div class="language-shell highlighter-rouge"><pre 
class="highlight"><code><span class="k">${</span><span 
class="nv">__setProperty</span><span class="p">(Authorization,</span><span 
class="k">${</span><span class="nv">Authorization</span><span 
class="k">}</span><span class="p">,)</span><span class="k">}</span>
+</code></pre>
+</div>
+<p>  在测试计划的最后部分就是我们要在Company示例上进行的压力测试。测试所选取的三个接口都是通过经理服务路由至å
…¶ä»–两个服务的,即技工服务和å…
»èœ‚人服务。在我们测试开始之前,我们通过打开<em>StressTest</em>的开å
…³æ¥ç¦ç”¨ç»ç†æœåŠ¡æä¾›çš„缓存能力,从而使得技工服务和å…
»èœ‚人服务能够处理到用户请求的计算任务。此外,我们通过将请求参数设置为1来简化技工服务和å
…»èœ‚人服务的计算任务。</p>
+
+<h2 id="启动测试">启动测试</h2>
+
+<ul>
+  <li>
+    <p>在Kubernetes集群中以无
资源限制的方式来运行<em>Company应用</em>。</p>
+  </li>
+  <li>
+    <p>修改hosts.csv文件,使其匹é…
æ­£åœ¨Kubernetes集群中运行的Company应用的服务地址。å…
¶ä¸­ï¼Œé»˜è®¤çš„hosts.csv文件内容为:</p>
+
+    <pre><code class="language-csv"> 127.0.0.1,8083
+</code></pre>
+  </li>
+  <li>
+    
<p>运行测试,启动200个并发线程发起请求压力,并设置测试时常为600秒。</p>
+
+    <div class="language-bash highlighter-rouge"><pre class="highlight"><code> 
jmeter -n -t workshop.jmx -j workshop.log -l workshop.jtl -Jthreads<span 
class="o">=</span>200 -Jduration<span class="o">=</span>600
+</code></pre>
+    </div>
+  </li>
+</ul>
+
+<h2 id="测试结果">测试结果</h2>
+<p>  在不同并发度下的测试结果如下图所示:</p>
+
+<p class="figure-caption"><img 
src="/assets/images/company_concurrency_performance.png" alt="图2 
并发不同时的性能比较" class="align-center" />
+图2 并发不同时的性能比较</p>
+
+<p>  可以看出,经理服务的性能在到达瓶颈(15并发度)前非常稳定,保持平均响应时间极低的æƒ
…
况下吞吐量快速上升到<strong>最大约1000请求每秒</strong>的水平。
 但随着并发度的进一步提升,平均响应时间开始快速增加
。因此,<strong>将响应时间统计数据作为评估熔断超
时的设置非常合适</strong>。</p>
+
+<p class="figure-caption"><img src="/assets/images/company_response_time.png" 
alt="图3 不同服务的平均响应时间" class="align-center" />
+图3 不同服务的平均响应时间</p>
+
+<p>  上图显示了不同服务的平均响应时间。由于å…
»èœ‚人服务需要调用技工服务,因此å…
¶å“åº”时间相对于技工服务的响应时间要稍微久一点。</p>
+
+<p class="figure-caption"><img src="/assets/images/company_cpu_load.png" 
alt="图4 不同并发度下CPU的负载" class="align-center" />
+图4 不同并发度下CPU的负载</p>
+
+<p>  为了找出性能卡在了15并发度时的原因
,我们回看了<a 
href="https://github.com/kubernetes/heapster";>Heapster</a>上的监控数据。如下图所示,显然,<strong>经理服务是当前系统的瓶颈所在</strong>。它在吞吐量为1000
 req/s时达到了最大的CPU负载。 相对而言,å…
¶å®ƒæœåŠ¡å¯¹èµ„源的需求的增长速度要慢得多。</p>
+
+<p>  由于经理服务的日志是直接输出到stdout上的,且JMeter的测试端以单机模式运行时可能并不能同时模拟出足够的并发量。依此对在同一并发度(200)下不同log的设置(log4j1
 stdout, log4j2 stdout,log4j2 异步,无日志输出)进行测试。å…
¶ä¸­ï¼Œå¼‚æ­¥log在<em>log4j2.xml</em>文件中的设置如下所示:</p>
+
+<div class="language-xml highlighter-rouge"><pre class="highlight"><code><span 
class="cp">&lt;?xml version="1.0" encoding="UTF-8" ?&gt;</span>
+<span class="nt">&lt;Configuration</span> <span class="na">status=</span><span 
class="s">"INFO"</span><span class="nt">&gt;</span>
+  <span class="nt">&lt;Appenders&gt;</span>
+    <span class="nt">&lt;RandomAccessFile</span> <span 
class="na">name=</span><span class="s">"RandomAccessFile"</span> <span 
class="na">fileName=</span><span class="s">"manager.log"</span> <span 
class="na">immediateFlush=</span><span class="s">"false"</span> <span 
class="na">append=</span><span class="s">"false"</span><span 
class="nt">&gt;</span>
+      <span class="nt">&lt;PatternLayout</span> <span 
class="na">pattern=</span><span class="s">"%d [%p] %m %l%n"</span><span 
class="nt">/&gt;</span>
+    <span class="nt">&lt;/RandomAccessFile&gt;</span>
+  <span class="nt">&lt;/Appenders&gt;</span>
+  <span class="nt">&lt;Loggers&gt;</span>
+    <span class="nt">&lt;asyncRoot</span> <span class="na">level=</span><span 
class="s">"info"</span><span class="nt">&gt;</span>
+      <span class="nt">&lt;AppenderRef</span> <span 
class="na">ref=</span><span class="s">"RandomAccessFile"</span><span 
class="nt">/&gt;</span>
+    <span class="nt">&lt;/asyncRoot&gt;</span>
+  <span class="nt">&lt;/Loggers&gt;</span>
+<span class="nt">&lt;/Configuration&gt;</span>
+</code></pre>
+</div>
+<p>此外,还需要添加
如下<em>disruptor</em>的依赖项使异步的设置生效:</p>
+
+<div class="language-xml highlighter-rouge"><pre class="highlight"><code><span 
class="nt">&lt;dependency&gt;</span>
+  <span class="nt">&lt;groupId&gt;</span>com.lmax<span 
class="nt">&lt;/groupId&gt;</span>
+  <span class="nt">&lt;artifactId&gt;</span>disruptor<span 
class="nt">&lt;/artifactId&gt;</span>
+  <span class="nt">&lt;version&gt;</span>3.3.6<span 
class="nt">&lt;/version&gt;</span>
+<span class="nt">&lt;/dependency&gt;</span>
+</code></pre>
+</div>
+<p>没有日志输出的设置仅仅
是将上述<em>log4j2.xml</em>文件中的日志输出级别从<em>info</em>改为<em>off</em>即可。此外,我们还使用JMeter分布式的模式进行了相å
…³çš„测试。JMeter中使用分布式的模式来运行主要分两步:</p>
+
+<ol>
+  <li>
+    <p>在每个测试从节点上运行<em>jmeter-server</em>,å…
¶è¿è¡ŒæŒ‡ä»¤å¦‚下:</p>
+
+    <div class="language-bash highlighter-rouge"><pre 
class="highlight"><code>jmeter-server -Djava.rmi.server.hostname<span 
class="o">=</span><span class="k">$(</span>ifconfig eth0 | grep <span 
class="s2">"inet addr"</span> | awk <span class="s1">'{print $2}'</span> | cut 
-d <span class="s2">":"</span> -f2<span class="k">)</span>
+</code></pre>
+    </div>
+  </li>
+  <li>
+    <p>在测试主节点上运行<em>jmeter</em>,指令如下:</p>
+
+    <div class="language-bash highlighter-rouge"><pre 
class="highlight"><code>jmeter -n -R host1,host2 -t workshop.jmx -j 
workshop.log -l workshop.jtl -Gmin<span class="o">=</span>1 -Gmax<span 
class="o">=</span>2 -Gthreads<span class="o">=</span>200 -Gduration<span 
class="o">=</span>600
+</code></pre>
+    </div>
+
+    <p 
class="notice--warning"><em>注意事项</em>:JMeter属性在分布式模式下并不能生效,需要将å
…¶å£°æ˜Žä¸ºå…¨å±€çš„属性。因
此,此处我们用的是<em>-G</em>的选项而不是之前的<em>-J</em>的选项。</p>
+  </li>
+</ol>
+
+<p>运行结果如下所示:</p>
+
+<p><img src="/assets/images/company_log_and_jmeter.png" 
alt="不同日志设置不同模式下的性能" class="align-center" /></p>
+
+<p>  从上图可以看出:</p>
+
+<ul>
+  <li>
+    
<p>JMeter单机测试和分布式测试的性能都非常接近,说明单机模式的JMeter测试暂时来说是足以模拟出足够的并发数来处理当前的测试场景的。</p>
+  </li>
+  <li>
+    <p>日志的输出的确对系统的性能造
成了较大的影响,可以看到,异步输出日志的方式能比同步输出的方式提升接近100%的吞吐量。å›
 æ­¤ï¼Œ<strong>在生产环境下使用完å…
¨åŒæ­¥è¾“出日志的方式可能并不会有较理想的性能。</strong></p>
+  </li>
+  <li>
+    <p>log4j2的吞吐量相对于log4j1而言大幅提高了约40%,内
存使用量也更少了。因
此,<strong>推荐使用性能更佳的log4j2替换掉陈旧的log4j1</strong>。</p>
+  </li>
+</ul>
+
+<p class="figure-caption"><img 
src="/assets/images/company_different_log_memory_usage.png" alt="图5 
不同日志设置下的内存使用量" class="align-center" />
+图5 不同日志设置下的内存使用量</p>
+
+<p>  尽管异步日志的方式能极大地提高系统的吞吐量,但它同时也å
 ç”¨äº†è¾ƒå¤šçš„内存,如图5所示。</p>
+
+<p class="figure-caption"><img src="/assets/images/company_memory_used.png" 
alt="图6 测试过程的内存使用量" class="align-center" />
+图6 测试过程的内存使用量</p>
+
+<p>  图6显示了在测试过程中不同服务的内
存使用量。由于Company示例只是一个简单的用例,在测试过程中各个服务的å†
…存使用率都相对稳定。然而,相对<em>告示æ 
</em>服务(使用go语言编写)的内存使用量而言,å…
¶å®ƒä»¥Java来编写的服务则占用了较多的内存。</p>
+
+<h2 id="总结">总结</h2>
+
+<p>  对应用进行压力测试往往能在应用投å…
¥ç”Ÿäº§çŽ¯å¢ƒå‰å¸®åŠ©æˆ‘们找出服务中潜在的问题。压力测试也能模拟生产环境,从而来验证服务是否已达到规定的性能指æ
 ‡ã€‚而æ 
¹æ®åŽ‹åŠ›æµ‹è¯•çš„结果,我们可以权衡Pod部署时的设置来保证SLA的同时获得最大的系统吞吐能力。</p>
+
+<p>  基于微服务架构的应用不仅在设计、编ç 
åŠæµ‹è¯•æ–¹é¢å˜å¾—更加灵活,同时也使得部署更加
方便。基于微服务架构能够保证资源的弹性伸缩极其迅
捷,我们可以æ 
¹æ®æœåŠ¡çš„真实可承受压力设置不同的副本数和资源é…
ç½®æ¥èŠ‚约资源。此外,在云上也能借助å…
¶å¼¹æ€§ä¼¸ç¼©èƒ½åŠ›ä½¿å¾—服务能够应对访问风暴。</p>
+
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