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| <h2 id="使用kubernetes对saga进行压力测试">使用Kubernetes对Saga进行压力测试</h2> |
| |
| <p>Apache ServiceComb (incubating) Saga 是一个微服务应用的数据最终一致性解决方案。Saga在try阶段直接提交事务，后续rollback阶段则通过反向的补偿操作来完成。</p> |
| |
| <p>基于ServiceComb Saga的项目，基本的构架如下：</p> |
| |
| <p><img src="https://raw.githubusercontent.com/apache/incubator-servicecomb-saga/master/docs/static_files/pack.png" alt="overview" /></p> |
| |
| <p>在我们的Saga实现中，业务服务引入Omega库，通过Omega将事务相关信息作为事件发送给Alpha server，由Alpha server统一进行协调。Alpha server将事务保存在PostgreSQL中，后台定期进行扫描。当扫描到异常事件时，尝试向事件对应的Omega发送gRPC请求，调用补偿方法。由于在原生业务中加入了Omega，进行了一系列后台操作，因此需要对整个框架进行压力测试，以获取框架的基础性能报告。</p> |
| |
| <p>在Cloud Native时代，容器几乎是标准的部署形态，应用程序容器化之后，通过Kubernetes进行容器编排，可以轻松的实现弹性扩容、任务调度等，非常适合对该项目进行压力测试。有鉴于此，我们将demo项目构建成docker镜像，并部署到Kubernetes集群中，通过Kubernetes的一系列组件，对demo方便的进行压力测试，以对Saga项目的性能有一个初步的评估。</p> |
| |
| <h3 id="kubernetize服务">“Kubernetize”服务</h3> |
| |
| <p>我们的demo项目由Java编写、maven作为依赖管理工具，在项目中引入了fabric8插件，编译程序时可以顺便将jar包构建成docker镜像。首先，我们构建alpha-server镜像：</p> |
| |
| <div class="language-bash highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="nv">$ </span><span class="nb">cd </span>alpha/alpha-server |
| <span class="nv">$ </span>mvn clean <span class="nb">install</span> <span class="nt">-Pdocker</span> |
| </code></pre></div></div> |
| |
| <p>然后进入demo项目路径<code class="highlighter-rouge">saga-demo/saga-spring-demo</code>，执行相同的maven构建命令，构建完成后，我们可以看到产生了4个相关镜像：</p> |
| |
| <div class="language-bash highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="nv">$ </span>docker images | <span class="nb">grep </span>SNAPHOST <span class="c"># {version}-SNAPSHOT是构建过程中使用的镜像标签</span> |
| alpha-server:0.3.0-SNAPSHOT |
| booking:0.3.0-SNAPSHOT |
| car:0.3.0-SNAPSHOT |
| hotel:0.3.0-SNAPSHOT |
| </code></pre></div></div> |
| |
| <p>至此，我们已经构建好所需镜像，下一步便是编写Kubernetes所需的资源文件，这一步我们不再赘述，项目中已经有写好的yaml文件，路径在<code class="highlighter-rouge">saga-demo/saga-k8s-resources</code>，目录结构如下：</p> |
| |
| <pre><code class="language-base">. |
| ├── base |
| │ ├── alpha.yaml |
| │ ├── jmeter-collector.yaml |
| │ └── postgresql.yaml |
| ├── README.md |
| └── spring-demo |
| ├── booking.yaml |
| ├── car.yaml |
| ├── hotel.yaml |
| └── test |
| ├── jmeter.configmap.yaml |
| └── jmeter.yaml |
| </code></pre> |
| |
| <p>其中<code class="highlighter-rouge">base</code>目录包含了alpha-server，postgresql以及用于收集测试报告的jmeter-collector3个服务。<code class="highlighter-rouge">spring-demo</code>目录包含了demo项目的所有服务，<code class="highlighter-rouge">spring-demo/test</code>路径下包含了对demo测试所需的服务。</p> |
| |
| <p>我们通过最基础的deployment和service对项目进行部署，程序之间通过域名互相访问，通过<code class="highlighter-rouge">kubectl</code>命令对Kubernetes资源进行部署：</p> |
| |
| <div class="language-bash highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="nv">$ </span>kubectl create ns servicecomb <span class="c"># 默认所有服务都在servicecomb namespace下</span> |
| <span class="nv">$ </span>kubectl apply <span class="nt">-f</span> ./base |
| <span class="nv">$ </span>kubectl apply <span class="nt">-f</span> ./spring-demo |
| </code></pre></div></div> |
| |
| <p>我们通过<code class="highlighter-rouge">kubectl exec</code>命令进入一个pod，通过curl命令测试booking程序：</p> |
| |
| <div class="language-bash highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="nv">$ </span>kubectl <span class="nb">exec</span> <span class="nt">-it</span> <span class="nt">-n</span> servicecomb alpha-server-xxxxx |
| <span class="nv">$ </span>curl http://booking.servicecomb:8083/booking/test/2/2 |
| resp: OK |
| </code></pre></div></div> |
| |
| <p>至此，我们的demo项目就已经运行起来了。</p> |
| |
| <h3 id="部署jmeter">部署JMeter</h3> |
| |
| <p>我们选用<a href="https://jmeter.apache.org/">JMeter</a>作为压测工具，<a href="https://hub.docker.com/">Docker Hub</a>上已有公共镜像<a href="https://hub.docker.com/r/justb4/jmeter/">justb4/jmeter</a>，该镜像比较成熟，启动前会先侦测系统中可用内存，按照一定比例为jmeter申请jvm内存，可以最大化、合理的利用系统资源。</p> |
| |
| <p>当我们部署JMeter时，为了保持测试的灵活性，一般需要将JMeter脚本单独存储，而JMeter程序可以通过某种方式获取测试脚本，进而保证测试配置可以单独修改，不需要重新构建JMeter镜像。在Kubernetes中，我们可以将JMeter的配置文件存放于<a href="https://kubernetes.io/docs/tasks/configure-pod-container/configure-pod-configmap/">ConfigMap</a>，并通过<a href="https://kubernetes.io/docs/concepts/storage/volumes/">VolumeMount</a>挂载到JMeter容器的指定目录，这样，当我们需要修改测试配置时，只需修改ConfigMap，并重新部署JMeter deployment，即可完成配置的更新。</p> |
| |
| <p>部署ConfigMap命令如下：</p> |
| |
| <div class="language-bash highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="nv">$ </span>kubectl apply <span class="nt">-f</span> spring-demo/test/jmeter.configmap.yaml |
| </code></pre></div></div> |
| |
| <h3 id="jmeter-镜像改造">JMeter 镜像改造</h3> |
| |
| <p>由于原有的JMeter容器执行完测试之后就退出了，如果在单机环境、仅运行docker容器的环境下，我们可以在执行<code class="highlighter-rouge">docker run</code>命令时通过-v参数将宿主机目录挂载到容器中，以保存测试的结果。但是Kubernetes环境，每个pod部署时都是根据一定的算法分配到不同节点上的，节点即是容器的宿主机。但是，登录到节点并找到相应目录来查看结果似乎是非常“不体面”的姿势，因此需要构建一个服务，收集JMeter生成的报告，并能够方便的展示。为此，我们用go语言实现了一个简单的文件上传服务，并附带静态文件服务。该服务收到JMeter测试报告达成的tgz压缩包，将其解压到相应静态文件服务目录中，这样，我们就可以方便的在集群中查看测试结果。该服务就是上文提到的jmeter-collector服务。</p> |
| |
| <p>这样一来，我们还需要对JMeter服务进行一些小改造，当执行完JMeter测试后，我们通过一个脚本将测试结果目录打包上传到jmeter-collector服务。这些改造都已经完成并做好了相应的镜像，直接使用项目中的资源即可：</p> |
| |
| <div class="language-bash highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="nv">$ </span>kubectl apply <span class="nt">-f</span> spring-demo/test/jmeter.yaml |
| </code></pre></div></div> |
| |
| <p>现在，我们已经配置好JMeter相关资源，执行<code class="highlighter-rouge">kubectl logs</code>检查JMeter运行状况：</p> |
| |
| <div class="language-bash highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="nv">$ </span>kubectl get pod <span class="nt">-n</span> servicecomb | <span class="nb">grep </span>spring-demo-jmeter |
| spring-demo-jmeter-xxxxx |
| <span class="nv">$ </span>kubectl logs <span class="nt">-f</span> <span class="nt">-n</span> servicecomb pring-demo-jmeter-xxxxx |
| ... |
| summary + 420 <span class="k">in </span>00:00:22 <span class="o">=</span> 18.8/s Avg: 214 Min: 111 Max: 471 Err: 207 <span class="o">(</span>49.29%<span class="o">)</span> Active: 12 Started: 12 Finished: 0 |
| </code></pre></div></div> |
| |
| <p>可以看到JMeter已经开始逐步增加测试线程进行压测了。</p> |
| |
| <p>由于我们用deployment部署JMeter，当执行结束后，容器正常退出，Kubernetes会重新启动容器，将测试服务再次拉起。因此测试会持续进行，直到我们将deployment删除。</p> |
| |
| <p>至此，我们已经完成了demo和相应的压力测试服务，整个集群中相关资源的结构如下图所示：</p> |
| |
| <p><img src="https://github.com/crystaldust/incubator-servicecomb-website/raw/blogs/loadtest-saga-with-k8s/assets/images/spring-demo.jpg" alt="demo-and-test-arch" /></p> |
| |
| <p>JMeter服务通过ConfigMap读取测试脚本，然后对booking服务发起压力测试，测试结束后，将结果上传至jmeter-collector服务。</p> |
| |
| <h3 id="查看测试结果">查看测试结果</h3> |
| |
| <p>JMeter容器运行完成后，在容器内生成了测试结果文件，并调用upload脚本打包上传到jmeter-collector服务，可以通过该服务直接在页面上查看结果。在Kubernetes中，在集群外部访问服务有两种方式：LoadBalancer或NodePort，前者一般由提供Kubernetes集群服务的云服务厂商提供，后者则可以通过节点的IP和端口来访问某个服务。由于我们使用了本地部署的Kubernetes集群，因此我们使用NodePort来查看访问结果。</p> |
| |
| <p>首先，调用<code class="highlighter-rouge">kubectl edit</code>命令，编辑jmeter-collector服务：</p> |
| |
| <div class="language-bash highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="nv">$ </span>kubectl edit svc <span class="nt">-n</span> servicecomb jmeter-collector |
| <span class="c"># in the editor:</span> |
| spec: |
| <span class="nb">type</span>: NodePort <span class="c">## 插入此行，注意yaml文件空格缩进</span> |
| exteranlIPs: <span class="o">[</span><span class="s2">"192.168.43.70"</span><span class="o">]</span> <span class="c">## 插入此行，根据Kubernetes集群中节点IP来设置</span> |
| ports: |
| - port: 80 |
| targetPort: 8883 |
| <span class="c">#....</span> |
| </code></pre></div></div> |
| |
| <p>在笔者的环境中，制定了IP为192.168.43.70的节点作为NodePort的IP，对外暴露80端口，因此，在浏览器中直接访问<code class="highlighter-rouge">http://192.168.43.70</code>即可查看测试报告的结果了，结果如下图所示：</p> |
| |
| <p><img src="https://github.com/crystaldust/incubator-servicecomb-website/raw/blogs/loadtest-saga-with-k8s/assets/images/jmeter-collector.png" alt="jmeter-collector-dashboard" /></p> |
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