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序言
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1. Kubernetes与云原生应用概览
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2. 概念原理
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2.1 设计理念
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2.2 Objects
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2.2.1 Pod
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2.2.1.1 Pod解析
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2.2.1.2 Init容器
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2.2.1.3 Pod安全策略
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2.2.1.4 Pod的生命周期
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2.2.2 Node
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2.2.3 Namespace
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2.2.4 Service
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2.2.5 Volume和Persistent Volume
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2.2.6 Deployment
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2.2.7 Secret
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2.2.8 StatefulSet
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2.2.9 DaemonSet
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2.2.10 ServiceAccount
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2.2.11 ReplicationController和ReplicaSet
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2.2.12 Job
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2.2.13 CronJob
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2.2.14 Ingress
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2.2.15 ConfigMap
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2.2.16 Horizontal Pod Autoscaling
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2.2.17 Label
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2.2.18 垃圾收集
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2.2.19 NetworkPolicy
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3. 用户指南
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3.1 资源对象配置
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3.1.1 配置Pod的liveness和readiness探针
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3.1.2 配置Pod的Service Account
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3.1.3 Secret配置
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3.2.3 管理namespace中的资源配额
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3.2 命令使用
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3.2.1 docker用户过度到kubectl命令行指南
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3.2.2 kubectl命令概览
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3.2.3 kubectl命令技巧大全
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3.3 集群安全性管理
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3.3.1 管理集群中的TLS
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3.3.2 kubelet的认证授权
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3.3.3 TLS bootstrap
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3.3.4 创建用户认证授权的kubeconfig文件
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3.3.5 RBAC——基于角色的访问控制
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3.3.6 IP伪装代理
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3.4 访问 Kubernetes 集群
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3.4.1 访问集群
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3.4.2 使用 kubeconfig 文件配置跨集群认证
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3.4.3 通过端口转发访问集群中的应用程序
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3.4.4 使用 service 访问群集中的应用程序
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3.5 在kubernetes中开发部署应用
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3.5.1 适用于kubernetes的应用开发部署流程
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3.5.2 迁移传统应用到kubernetes中——以Hadoop YARN为例
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3.5.3 使用StatefulSet部署用状态应用
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4. 最佳实践
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4.1 在CentOS上部署kubernetes1.6集群
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< a href = "create-tls-and-secret-key.html" >
4.1.1 创建TLS证书和秘钥
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4.1.2 创建kubeconfig文件
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< a href = "etcd-cluster-installation.html" >
4.1.3 创建高可用etcd集群
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< li class = "chapter " data-level = "1.5.1.4" data-path = "kubectl-installation.html" >
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< a href = "kubectl-installation.html" >
4.1.4 安装kubectl命令行工具
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< li class = "chapter " data-level = "1.5.1.5" data-path = "master-installation.html" >
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< a href = "master-installation.html" >
4.1.5 部署master节点
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< a href = "node-installation.html" >
4.1.6 部署node节点
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< li class = "chapter " data-level = "1.5.1.7" data-path = "kubedns-addon-installation.html" >
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< a href = "kubedns-addon-installation.html" >
4.1.7 安装kubedns插件
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< li class = "chapter " data-level = "1.5.1.8" data-path = "dashboard-addon-installation.html" >
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< a href = "dashboard-addon-installation.html" >
4.1.8 安装dashboard插件
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< a href = "heapster-addon-installation.html" >
4.1.9 安装heapster插件
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< li class = "chapter " data-level = "1.5.1.10" data-path = "efk-addon-installation.html" >
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< a href = "efk-addon-installation.html" >
4.1.10 安装EFK插件
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< a href = "service-discovery-and-loadbalancing.html" >
4.2 服务发现与负载均衡
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< li class = "chapter " data-level = "1.5.2.1" data-path = "traefik-ingress-installation.html" >
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< a href = "traefik-ingress-installation.html" >
4.2.1 安装Traefik ingress
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< li class = "chapter " data-level = "1.5.2.2" data-path = "distributed-load-test.html" >
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4.2.2 分布式负载测试
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< a href = "network-and-cluster-perfermance-test.html" >
4.2.3 网络和集群性能测试
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< li class = "chapter " data-level = "1.5.2.4" data-path = "edge-node-configuration.html" >
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< a href = "edge-node-configuration.html" >
4.2.4 边缘节点配置
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< a href = "nginx-ingress-installation.html" >
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4.2.5 安装Nginx ingress
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< a href = "operation.html" >
4.3 运维管理
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4.3.1 服务滚动升级
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< li class = "chapter " data-level = "1.5.3.2" data-path = "app-log-collection.html" >
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4.3.2 应用日志收集
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< li class = "chapter " data-level = "1.5.3.3" data-path = "configuration-best-practice.html" >
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< a href = "configuration-best-practice.html" >
4.3.3 配置最佳实践
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< a href = "monitor.html" >
4.3.4 集群及应用监控
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< a href = "data-persistence-problem.html" >
4.3.6 数据持久化问题
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< li class = "chapter " data-level = "1.5.3.6" data-path = "manage-compute-resources-container.html" >
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< a href = "manage-compute-resources-container.html" >
4.3.7 管理容器的计算资源
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< li class = "chapter " data-level = "1.5.3.7" data-path = "using-prometheus-to-monitor-kuberentes-cluster.html" >
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< a href = "using-prometheus-to-monitor-kuberentes-cluster.html" >
4.3.8 使用Prometheus监控kubernetes集群
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< a href = "using-heapster-to-get-object-metrics.html" >
4.3.9 使用Heapster获取集群和对象的metric数据
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4.4 存储管理
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< a href = "glusterfs.html" >
4.4.1 GlusterFS
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< a href = "using-glusterfs-for-persistent-storage.html" >
4.4.1.1 使用GlusterFS做持久化存储
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< li class = "chapter " data-level = "1.5.4.1.2" data-path = "storage-for-containers-using-glusterfs-with-openshift.html" >
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< a href = "storage-for-containers-using-glusterfs-with-openshift.html" >
4.4.1.2 在OpenShift中使用GlusterFS做持久化存储
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< a href = "cephfs.html" >
4.4.2 CephFS
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< a href = "using-ceph-for-persistent-storage.html" >
4.4.2.1 使用Ceph做持久化存储
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< li class = "chapter " data-level = "1.5.5" data-path = "services-management-tool.html" >
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< a href = "services-management-tool.html" >
4.5 服务编排管理
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< li class = "chapter " data-level = "1.5.5.1" data-path = "helm.html" >
< a href = "helm.html" >
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4.5.1 使用Helm管理kubernetes应用
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< li class = "chapter " data-level = "1.5.5.2" data-path = "create-private-charts-repo.html" >
< a href = "create-private-charts-repo.html" >
4.5.2 构建私有Chart仓库
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< a href = "ci-cd.html" >
4.6 持续集成与发布
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< a href = "jenkins-ci-cd.html" >
4.6.1 使用Jenkins进行持续集成与发布
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< li class = "chapter " data-level = "1.5.6.2" data-path = "drone-ci-cd.html" >
< a href = "drone-ci-cd.html" >
4.6.2 使用Drone进行持续集成与发布
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< a href = "update-and-upgrade.html" >
4.7 更新与升级
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< a href = "manually-upgrade.html" >
4.7.1 手动升级kubernetes集群
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< li class = "chapter " data-level = "1.5.7.2" data-path = "dashboard-upgrade.html" >
< a href = "dashboard-upgrade.html" >
4.7.2 升级dashboard
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< a href = "../usecases/" >
5. 领域应用
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< a href = "../usecases/microservices.html" >
5.1 微服务架构
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< a href = "../usecases/service-discovery-in-microservices.html" >
5.1.1 微服务中的服务发现
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< li class = "chapter " data-level = "1.6.2" data-path = "../usecases/service-mesh.html" >
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< a href = "../usecases/service-mesh.html" >
5.2 Service Mesh 服务网格
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< a href = "../usecases/istio.html" >
5.1.1 Istio
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< a href = "../usecases/istio-installation.html" >
5.1.1.1 安装istio
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< a href = "../usecases/configuring-request-routing.html" >
5.1.1.2 配置请求的路由规则
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< a href = "../usecases/linkerd.html" >
5.1.2 Linkerd
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< li class = "chapter " data-level = "1.6.2.2.1" data-path = "../usecases/linkerd-user-guide.html" >
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< a href = "../usecases/linkerd-user-guide.html" >
5.1.2.1 Linkerd 使用指南
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< a href = "../usecases/big-data.html" >
5.2 大数据
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< a href = "../usecases/spark-standalone-on-kubernetes.html" >
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5.2.1 Spark standalone on Kubernetes
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< li class = "chapter " data-level = "1.6.3.2" data-path = "../usecases/running-spark-with-kubernetes-native-scheduler.html" >
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< a href = "../usecases/running-spark-with-kubernetes-native-scheduler.html" >
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5.2.2 运行支持kubernetes原生调度的Spark程序
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< a href = "../usecases/serverless.html" >
5.3 Serverless架构
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< a href = "../usecases/edge-computing.html" >
5.4 边缘计算
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< a href = "../develop/" >
6. 开发指南
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< a href = "../develop/developing-environment.html" >
6.1 开发环境搭建
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< a href = "../develop/testing.html" >
6.2 单元测试和集成测试
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< li class = "chapter " data-level = "1.7.3" data-path = "../develop/client-go-sample.html" >
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< a href = "../develop/client-go-sample.html" >
6.3 client-go示例
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< a href = "../develop/contribute.html" >
6.4 社区贡献
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< a href = "../develop/minikube.html" >
6.5 Minikube
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7. 附录
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7.1 Docker最佳实践
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< a href = "../appendix/issues.html" >
7.2 问题记录
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7.3 使用技巧
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< a href = "../appendix/debug-kubernetes-services.html" >
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7.4 kubernetes中的应用故障排查
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< a href = "../appendix/material-share.html" >
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7.5 Kubernetes相关资讯和情报链接
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< / ul >
< / li >
< li class = "divider" > < / li >
< li >
< a href = "https://www.gitbook.com" target = "blank" class = "gitbook-link" >
2017-09-19 22:01:07 +08:00
本书使用 GitBook 发布
2017-08-21 17:44:43 +08:00
< / a >
< / li >
< / ul >
< / nav >
< / div >
< div class = "book-body" >
< div class = "body-inner" >
< div class = "book-header" role = "navigation" >
<!-- Title -->
< h1 >
< i class = "fa fa-circle-o-notch fa-spin" > < / i >
< a href = ".." > 4.2.3 网络和集群性能测试< / a >
< / h1 >
< / div >
< div class = "page-wrapper" tabindex = "-1" role = "main" >
< div class = "page-inner" >
< div class = "search-plus" id = "book-search-results" >
< div class = "search-noresults" >
< section class = "normal markdown-section" >
< h1 id = "kubernetes网络和集群性能测试" > Kubernetes网 络 和 集 群 性 能 测 试 < / h1 >
< h2 id = "准备" > 准 备 < / h2 >
< p > < strong > 测 试 环 境 < / strong > < / p >
< p > 在 以 下 几 种 环 境 下 进 行 测 试 ： < / p >
< ul >
< li > Kubernetes集 群 node节 点 上 通 过 Cluster IP方 式 访 问 < / li >
< li > Kubernetes集 群 内 部 通 过 service访 问 < / li >
< li > Kubernetes集 群 外 部 通 过 traefik ingress暴 露 的 地 址 访 问 < / li >
< / ul >
< p > < strong > 测 试 地 址 < / strong > < / p >
< p > Cluster IP: 10.254.149.31< / p >
< p > Service Port： 8000< / p >
< p > Ingress Host： traefik.sample-webapp.io< / p >
< p > < strong > 测 试 工 具 < / strong > < / p >
< ul >
< li > < a href = "http://locust.io" target = "_blank" > Locust< / a > ： 一 个 简 单 易 用 的 用 户 负 载 测 试 工 具 ， 用 来 测 试 web或 其 他 系 统 能 够 同 时 处 理 的 并 发 用 户 数 。 < / li >
< li > curl< / li >
< li > < a href = "https://github.com/kubernetes/kubernetes/tree/master/test/e2e" target = "_blank" > kubemark< / a > < / li >
< li > 测 试 程 序 ： sample-webapp， 源 码 见 Github < a href = "https://github.com/rootsongjc/distributed-load-testing-using-kubernetes" target = "_blank" > kubernetes的 分 布 式 负 载 测 试 < / a > < / li >
< / ul >
< p > < strong > 测 试 说 明 < / strong > < / p >
< p > 通 过 向 < code > sample-webapp< / code > 发 送 curl请 求 获 取 响 应 时 间 ， 直 接 curl后 的 结 果 为 ： < / p >
< pre > < code class = "lang-Bash" > $ curl < span class = "hljs-string" > " http://10.254.149.31:8000/" < / span >
Welcome to the < span class = "hljs-string" > " Distributed Load Testing Using Kubernetes" < / span > sample web app
< / code > < / pre >
< h2 id = "网络延迟测试" > 网 络 延 迟 测 试 < / h2 >
< h3 id = "场景一、-kubernetes集群node节点上通过cluster-ip访问" > 场 景 一 、 Kubernetes集 群 node节 点 上 通 过 Cluster IP访 问 < / h3 >
< p > < strong > 测 试 命 令 < / strong > < / p >
< pre > < code class = "lang-shell" > curl -o /dev/null -s -w ' %{time_connect} %{time_starttransfer} %{time_total}' " http://10.254.149.31:8000/"
< / code > < / pre >
< p > < strong > 10组 测 试 结 果 < / strong > < / p >
< table >
< thead >
< tr >
< th > No< / th >
< th > time_connect< / th >
< th > time_starttransfer< / th >
< th > time_total< / th >
< / tr >
< / thead >
< tbody >
< tr >
< td > 1< / td >
< td > 0.000< / td >
< td > 0.003< / td >
< td > 0.003< / td >
< / tr >
< tr >
< td > 2< / td >
< td > 0.000< / td >
< td > 0.002< / td >
< td > 0.002< / td >
< / tr >
< tr >
< td > 3< / td >
< td > 0.000< / td >
< td > 0.002< / td >
< td > 0.002< / td >
< / tr >
< tr >
< td > 4< / td >
< td > 0.000< / td >
< td > 0.002< / td >
< td > 0.002< / td >
< / tr >
< tr >
< td > 5< / td >
< td > 0.000< / td >
< td > 0.002< / td >
< td > 0.002< / td >
< / tr >
< tr >
< td > 6< / td >
< td > 0.000< / td >
< td > 0.002< / td >
< td > 0.002< / td >
< / tr >
< tr >
< td > 7< / td >
< td > 0.000< / td >
< td > 0.002< / td >
< td > 0.002< / td >
< / tr >
< tr >
< td > 8< / td >
< td > 0.000< / td >
< td > 0.002< / td >
< td > 0.002< / td >
< / tr >
< tr >
< td > 9< / td >
< td > 0.000< / td >
< td > 0.002< / td >
< td > 0.002< / td >
< / tr >
< tr >
< td > 10< / td >
< td > 0.000< / td >
< td > 0.002< / td >
< td > 0.002< / td >
< / tr >
< / tbody >
< / table >
< p > < strong > 平 均 响 应 时 间 ： 2ms< / strong > < / p >
< p > < strong > 时 间 指 标 说 明 < / strong > < / p >
< p > 单 位 ： 秒 < / p >
< p > time_connect： 建 立 到 服 务 器 的 TCP 连 接 所 用 的 时 间 < / p >
< p > time_starttransfer： 在 发 出 请 求 之 后 ， Web 服 务 器 返 回 数 据 的 第 一 个 字 节 所 用 的 时 间 < / p >
< p > time_total： 完 成 请 求 所 用 的 时 间 < / p >
< h3 id = "场景二、kubernetes集群内部通过service访问" > 场 景 二 、 Kubernetes集 群 内 部 通 过 service访 问 < / h3 >
< p > < strong > 测 试 命 令 < / strong > < / p >
< pre > < code class = "lang-Shell" > curl -o /dev/null -s -w ' %{time_connect} %{time_starttransfer} %{time_total}' " http://sample-webapp:8000/"
< / code > < / pre >
< p > < strong > 10组 测 试 结 果 < / strong > < / p >
< table >
< thead >
< tr >
< th > No< / th >
< th > time_connect< / th >
< th > time_starttransfer< / th >
< th > time_total< / th >
< / tr >
< / thead >
< tbody >
< tr >
< td > 1< / td >
< td > 0.004< / td >
< td > 0.006< / td >
< td > 0.006< / td >
< / tr >
< tr >
< td > 2< / td >
< td > 0.004< / td >
< td > 0.006< / td >
< td > 0.006< / td >
< / tr >
< tr >
< td > 3< / td >
< td > 0.004< / td >
< td > 0.006< / td >
< td > 0.006< / td >
< / tr >
< tr >
< td > 4< / td >
< td > 0.004< / td >
< td > 0.006< / td >
< td > 0.006< / td >
< / tr >
< tr >
< td > 5< / td >
< td > 0.004< / td >
< td > 0.006< / td >
< td > 0.006< / td >
< / tr >
< tr >
< td > 6< / td >
< td > 0.004< / td >
< td > 0.006< / td >
< td > 0.006< / td >
< / tr >
< tr >
< td > 7< / td >
< td > 0.004< / td >
< td > 0.006< / td >
< td > 0.006< / td >
< / tr >
< tr >
< td > 8< / td >
< td > 0.004< / td >
< td > 0.006< / td >
< td > 0.006< / td >
< / tr >
< tr >
< td > 9< / td >
< td > 0.004< / td >
< td > 0.006< / td >
< td > 0.006< / td >
< / tr >
< tr >
< td > 10< / td >
< td > 0.004< / td >
< td > 0.006< / td >
< td > 0.006< / td >
< / tr >
< / tbody >
< / table >
< p > < strong > 平 均 响 应 时 间 ： 6ms< / strong > < / p >
< h3 id = "场景三、在公网上通过traefik-ingress访问" > 场 景 三 、 在 公 网 上 通 过 traefik ingress访 问 < / h3 >
< p > < strong > 测 试 命 令 < / strong > < / p >
< pre > < code class = "lang-Shell" > curl -o /dev/null -s -w ' %{time_connect} %{time_starttransfer} %{time_total}' " http://traefik.sample-webapp.io" > > result
< / code > < / pre >
< p > < strong > 10组 测 试 结 果 < / strong > < / p >
< table >
< thead >
< tr >
< th > No< / th >
< th > time_connect< / th >
< th > time_starttransfer< / th >
< th > time_total< / th >
< / tr >
< / thead >
< tbody >
< tr >
< td > 1< / td >
< td > 0.043< / td >
< td > 0.085< / td >
< td > 0.085< / td >
< / tr >
< tr >
< td > 2< / td >
< td > 0.052< / td >
< td > 0.093< / td >
< td > 0.093< / td >
< / tr >
< tr >
< td > 3< / td >
< td > 0.043< / td >
< td > 0.082< / td >
< td > 0.082< / td >
< / tr >
< tr >
< td > 4< / td >
< td > 0.051< / td >
< td > 0.093< / td >
< td > 0.093< / td >
< / tr >
< tr >
< td > 5< / td >
< td > 0.068< / td >
< td > 0.188< / td >
< td > 0.188< / td >
< / tr >
< tr >
< td > 6< / td >
< td > 0.049< / td >
< td > 0.089< / td >
< td > 0.089< / td >
< / tr >
< tr >
< td > 7< / td >
< td > 0.051< / td >
< td > 0.113< / td >
< td > 0.113< / td >
< / tr >
< tr >
< td > 8< / td >
< td > 0.055< / td >
< td > 0.120< / td >
< td > 0.120< / td >
< / tr >
< tr >
< td > 9< / td >
< td > 0.065< / td >
< td > 0.126< / td >
< td > 0.127< / td >
< / tr >
< tr >
< td > 10< / td >
< td > 0.050< / td >
< td > 0.111< / td >
< td > 0.111< / td >
< / tr >
< / tbody >
< / table >
< p > < strong > 平 均 响 应 时 间 ： 110ms< / strong > < / p >
< h3 id = "测试结果" > 测 试 结 果 < / h3 >
< p > 在 这 三 种 场 景 下 的 响 应 时 间 测 试 结 果 如 下 ： < / p >
< ul >
< li > Kubernetes集 群 node节 点 上 通 过 Cluster IP方 式 访 问 ： 2ms< / li >
< li > Kubernetes集 群 内 部 通 过 service访 问 ： 6ms< / li >
< li > Kubernetes集 群 外 部 通 过 traefik ingress暴 露 的 地 址 访 问 ： 110ms< / li >
< / ul >
< p > < em > 注 意 ： 执 行 测 试 的 node节 点 /Pod与 serivce所 在 的 pod的 距 离 （ 是 否 在 同 一 台 主 机 上 ） ， 对 前 两 个 场 景 可 以 能 会 有 一 定 影 响 。 < / em > < / p >
< h2 id = "网络性能测试" > 网 络 性 能 测 试 < / h2 >
< p > 网 络 使 用 flannel的 vxlan模 式 。 < / p >
< p > 使 用 iperf进 行 测 试 。 < / p >
< p > 服 务 端 命 令 ： < / p >
< pre > < code class = "lang-shell" > iperf -s -p 12345 -i 1 -M
< / code > < / pre >
< p > 客 户 端 命 令 ： < / p >
< pre > < code class = "lang-shell" > iperf -c ${server-ip} -p 12345 -i 1 -t 10 -w 20K
< / code > < / pre >
< h3 id = "场景一、主机之间" > 场 景 一 、 主 机 之 间 < / h3 >
< pre > < code > [ ID] Interval Transfer Bandwidth
[ 3] 0.0- 1.0 sec 598 MBytes 5.02 Gbits/sec
[ 3] 1.0- 2.0 sec 637 MBytes 5.35 Gbits/sec
[ 3] 2.0- 3.0 sec 664 MBytes 5.57 Gbits/sec
[ 3] 3.0- 4.0 sec 657 MBytes 5.51 Gbits/sec
[ 3] 4.0- 5.0 sec 641 MBytes 5.38 Gbits/sec
[ 3] 5.0- 6.0 sec 639 MBytes 5.36 Gbits/sec
[ 3] 6.0- 7.0 sec 628 MBytes 5.26 Gbits/sec
[ 3] 7.0- 8.0 sec 649 MBytes 5.44 Gbits/sec
[ 3] 8.0- 9.0 sec 638 MBytes 5.35 Gbits/sec
[ 3] 9.0-10.0 sec 652 MBytes 5.47 Gbits/sec
[ 3] 0.0-10.0 sec 6.25 GBytes 5.37 Gbits/sec
< / code > < / pre > < h3 id = "场景二、不同主机的pod之间使用flannel的vxlan模式" > 场 景 二 、 不 同 主 机 的 Pod之 间 (使 用 flannel的 vxlan模 式 )< / h3 >
< pre > < code > [ ID] Interval Transfer Bandwidth
[ 3] 0.0- 1.0 sec 372 MBytes 3.12 Gbits/sec
[ 3] 1.0- 2.0 sec 345 MBytes 2.89 Gbits/sec
[ 3] 2.0- 3.0 sec 361 MBytes 3.03 Gbits/sec
[ 3] 3.0- 4.0 sec 397 MBytes 3.33 Gbits/sec
[ 3] 4.0- 5.0 sec 405 MBytes 3.40 Gbits/sec
[ 3] 5.0- 6.0 sec 410 MBytes 3.44 Gbits/sec
[ 3] 6.0- 7.0 sec 404 MBytes 3.39 Gbits/sec
[ 3] 7.0- 8.0 sec 408 MBytes 3.42 Gbits/sec
[ 3] 8.0- 9.0 sec 451 MBytes 3.78 Gbits/sec
[ 3] 9.0-10.0 sec 387 MBytes 3.25 Gbits/sec
[ 3] 0.0-10.0 sec 3.85 GBytes 3.30 Gbits/sec
< / code > < / pre > < h3 id = "场景三、node与非同主机的pod之间（使用flannel的vxlan模式）" > 场 景 三 、 Node与 非 同 主 机 的 Pod之 间 （ 使 用 flannel的 vxlan模 式 ） < / h3 >
< pre > < code > [ ID] Interval Transfer Bandwidth
[ 3] 0.0- 1.0 sec 372 MBytes 3.12 Gbits/sec
[ 3] 1.0- 2.0 sec 420 MBytes 3.53 Gbits/sec
[ 3] 2.0- 3.0 sec 434 MBytes 3.64 Gbits/sec
[ 3] 3.0- 4.0 sec 409 MBytes 3.43 Gbits/sec
[ 3] 4.0- 5.0 sec 382 MBytes 3.21 Gbits/sec
[ 3] 5.0- 6.0 sec 408 MBytes 3.42 Gbits/sec
[ 3] 6.0- 7.0 sec 403 MBytes 3.38 Gbits/sec
[ 3] 7.0- 8.0 sec 423 MBytes 3.55 Gbits/sec
[ 3] 8.0- 9.0 sec 376 MBytes 3.15 Gbits/sec
[ 3] 9.0-10.0 sec 451 MBytes 3.78 Gbits/sec
[ 3] 0.0-10.0 sec 3.98 GBytes 3.42 Gbits/sec
< / code > < / pre > < h3 id = "场景四、不同主机的pod之间（使用flannel的host-gw模式）" > 场 景 四 、 不 同 主 机 的 Pod之 间 （ 使 用 flannel的 host-gw模 式 ） < / h3 >
< pre > < code > [ ID] Interval Transfer Bandwidth
[ 5] 0.0- 1.0 sec 530 MBytes 4.45 Gbits/sec
[ 5] 1.0- 2.0 sec 576 MBytes 4.84 Gbits/sec
[ 5] 2.0- 3.0 sec 631 MBytes 5.29 Gbits/sec
[ 5] 3.0- 4.0 sec 580 MBytes 4.87 Gbits/sec
[ 5] 4.0- 5.0 sec 627 MBytes 5.26 Gbits/sec
[ 5] 5.0- 6.0 sec 578 MBytes 4.85 Gbits/sec
[ 5] 6.0- 7.0 sec 584 MBytes 4.90 Gbits/sec
[ 5] 7.0- 8.0 sec 571 MBytes 4.79 Gbits/sec
[ 5] 8.0- 9.0 sec 564 MBytes 4.73 Gbits/sec
[ 5] 9.0-10.0 sec 572 MBytes 4.80 Gbits/sec
[ 5] 0.0-10.0 sec 5.68 GBytes 4.88 Gbits/sec
< / code > < / pre > < h3 id = "场景五、node与非同主机的pod之间（使用flannel的host-gw模式）" > 场 景 五 、 Node与 非 同 主 机 的 Pod之 间 （ 使 用 flannel的 host-gw模 式 ） < / h3 >
< pre > < code > [ ID] Interval Transfer Bandwidth
[ 3] 0.0- 1.0 sec 570 MBytes 4.78 Gbits/sec
[ 3] 1.0- 2.0 sec 552 MBytes 4.63 Gbits/sec
[ 3] 2.0- 3.0 sec 598 MBytes 5.02 Gbits/sec
[ 3] 3.0- 4.0 sec 580 MBytes 4.87 Gbits/sec
[ 3] 4.0- 5.0 sec 590 MBytes 4.95 Gbits/sec
[ 3] 5.0- 6.0 sec 594 MBytes 4.98 Gbits/sec
[ 3] 6.0- 7.0 sec 598 MBytes 5.02 Gbits/sec
[ 3] 7.0- 8.0 sec 606 MBytes 5.08 Gbits/sec
[ 3] 8.0- 9.0 sec 596 MBytes 5.00 Gbits/sec
[ 3] 9.0-10.0 sec 604 MBytes 5.07 Gbits/sec
[ 3] 0.0-10.0 sec 5.75 GBytes 4.94 Gbits/sec
< / code > < / pre > < h3 id = "网络性能对比综述" > 网 络 性 能 对 比 综 述 < / h3 >
< p > 使 用 Flannel的 < strong > vxlan< / strong > 模 式 实 现 每 个 pod一 个 IP的 方 式 ， 会 比 宿 主 机 直 接 互 联 的 网 络 性 能 损 耗 30%～ 40%， 符 合 网 上 流 传 的 测 试 结 论 。 而 flannel的 host-gw模 式 比 起 宿 主 机 互 连 的 网 络 性 能 损 耗 大 约 是 10%。 < / p >
< p > Vxlan会 有 一 个 封 包 解 包 的 过 程 ， 所 以 会 对 网 络 性 能 造 成 较 大 的 损 耗 ， 而 host-gw模 式 是 直 接 使 用 路 由 信 息 ， 网 络 损 耗 小 ， 关 于 host-gw的 架 构 请 访 问 < a href = "https://docs.openshift.com/container-platform/3.4/architecture/additional_concepts/flannel.html" target = "_blank" > Flannel host-gw architecture< / a > 。 < / p >
< h2 id = "kubernete的性能测试" > Kubernete的 性 能 测 试 < / h2 >
< p > 参 考 < a href = "https://supereagle.github.io/2017/03/09/kubemark/" target = "_blank" > Kubernetes集 群 性 能 测 试 < / a > 中 的 步 骤 ， 对 kubernetes的 性 能 进 行 测 试 。 < / p >
< p > 我 的 集 群 版 本 是 Kubernetes1.6.0， 首 先 克 隆 代 码 ， 将 kubernetes目 录 复 制 到 < code > $GOPATH/src/k8s.io/< / code > 下 然 后 执 行 ： < / p >
< pre > < code class = "lang-bash" > $ ./hack/generate-bindata.sh
/usr/< span class = "hljs-built_in" > local< / span > /src/k8s.io/kubernetes /usr/< span class = "hljs-built_in" > local< / span > /src/k8s.io/kubernetes
Generated bindata file : < span class = "hljs-built_in" > test< / span > /e2e/generated/bindata.go has 13498 < span class = "hljs-built_in" > test< / span > /e2e/generated/bindata.go lines of lovely automated artifacts
No changes < span class = "hljs-keyword" > in< / span > generated bindata file: pkg/generated/bindata.go
/usr/< span class = "hljs-built_in" > local< / span > /src/k8s.io/kubernetes
$ make WHAT=< span class = "hljs-string" > " test/e2e/e2e.test" < / span >
...
+++ [0425 17:01:34] Generating bindata:
< span class = "hljs-built_in" > test< / span > /e2e/generated/gobindata_util.go
/usr/< span class = "hljs-built_in" > local< / span > /src/k8s.io/kubernetes /usr/< span class = "hljs-built_in" > local< / span > /src/k8s.io/kubernetes/< span class = "hljs-built_in" > test< / span > /e2e/generated
/usr/< span class = "hljs-built_in" > local< / span > /src/k8s.io/kubernetes/< span class = "hljs-built_in" > test< / span > /e2e/generated
+++ [0425 17:01:34] Building go targets < span class = "hljs-keyword" > for< / span > linux/amd64:
< span class = "hljs-built_in" > test< / span > /e2e/e2e.test
$ make ginkgo
+++ [0425 17:05:57] Building the toolchain targets:
k8s.io/kubernetes/hack/cmd/teststale
k8s.io/kubernetes/vendor/github.com/jteeuwen/go-bindata/go-bindata
+++ [0425 17:05:57] Generating bindata:
< span class = "hljs-built_in" > test< / span > /e2e/generated/gobindata_util.go
/usr/< span class = "hljs-built_in" > local< / span > /src/k8s.io/kubernetes /usr/< span class = "hljs-built_in" > local< / span > /src/k8s.io/kubernetes/< span class = "hljs-built_in" > test< / span > /e2e/generated
/usr/< span class = "hljs-built_in" > local< / span > /src/k8s.io/kubernetes/< span class = "hljs-built_in" > test< / span > /e2e/generated
+++ [0425 17:05:58] Building go targets < span class = "hljs-keyword" > for< / span > linux/amd64:
vendor/github.com/onsi/ginkgo/ginkgo
$ < span class = "hljs-built_in" > export< / span > KUBERNETES_PROVIDER=< span class = "hljs-built_in" > local< / span >
$ < span class = "hljs-built_in" > export< / span > KUBECTL_PATH=/usr/bin/kubectl
$ go run hack/e2e.go -v -test --test_args=< span class = "hljs-string" > " --host=http://172.20.0.113:8080 --ginkgo.focus=\[Feature:Performance\]" < / span > > > log.txt
< / code > < / pre >
< p > < strong > 测 试 结 果 < / strong > < / p >
< pre > < code class = "lang-bash" > Apr 25 18:27:31.461: INFO: API calls latencies: {
< span class = "hljs-string" > " apicalls" < / span > : [
{
< span class = "hljs-string" > " resource" < / span > : < span class = "hljs-string" > " pods" < / span > ,
< span class = "hljs-string" > " verb" < / span > : < span class = "hljs-string" > " POST" < / span > ,
< span class = "hljs-string" > " latency" < / span > : {
< span class = "hljs-string" > " Perc50" < / span > : 2148000,
< span class = "hljs-string" > " Perc90" < / span > : 13772000,
< span class = "hljs-string" > " Perc99" < / span > : 14436000,
< span class = "hljs-string" > " Perc100" < / span > : 0
}
},
{
< span class = "hljs-string" > " resource" < / span > : < span class = "hljs-string" > " services" < / span > ,
< span class = "hljs-string" > " verb" < / span > : < span class = "hljs-string" > " DELETE" < / span > ,
< span class = "hljs-string" > " latency" < / span > : {
< span class = "hljs-string" > " Perc50" < / span > : 9843000,
< span class = "hljs-string" > " Perc90" < / span > : 11226000,
< span class = "hljs-string" > " Perc99" < / span > : 12391000,
< span class = "hljs-string" > " Perc100" < / span > : 0
}
},
...
Apr 25 18:27:31.461: INFO: [Result:Performance] {
< span class = "hljs-string" > " version" < / span > : < span class = "hljs-string" > " v1" < / span > ,
< span class = "hljs-string" > " dataItems" < / span > : [
{
< span class = "hljs-string" > " data" < / span > : {
< span class = "hljs-string" > " Perc50" < / span > : 2.148,
< span class = "hljs-string" > " Perc90" < / span > : 13.772,
< span class = "hljs-string" > " Perc99" < / span > : 14.436
},
< span class = "hljs-string" > " unit" < / span > : < span class = "hljs-string" > " ms" < / span > ,
< span class = "hljs-string" > " labels" < / span > : {
< span class = "hljs-string" > " Resource" < / span > : < span class = "hljs-string" > " pods" < / span > ,
< span class = "hljs-string" > " Verb" < / span > : < span class = "hljs-string" > " POST" < / span >
}
},
...
2.857: INFO: Running AfterSuite actions on all node
Apr 26 10:35:32.857: INFO: Running AfterSuite actions on node 1
Ran 2 of 606 Specs < span class = "hljs-keyword" > in< / span > 268.371 seconds
SUCCESS! -- 2 Passed | 0 Failed | 0 Pending | 604 Skipped PASS
Ginkgo ran 1 suite < span class = "hljs-keyword" > in< / span > 4m28.667870101s
Test Suite Passed
< / code > < / pre >
< p > 从 kubemark输 出 的 日 志 中 可 以 看 到 < strong > API calls latencies< / strong > 和 < strong > Performance< / strong > 。 < / p >
< p > < strong > 日 志 里 显 示 ， 创 建 90个 pod用 时 40秒 以 内 ， 平 均 创 建 每 个 pod耗 时 0.44秒 。 < / strong > < / p >
< h3 id = "不同type的资源类型api请求耗时分布" > 不 同 type的 资 源 类 型 API请 求 耗 时 分 布 < / h3 >
< table >
< thead >
< tr >
< th > Resource< / th >
< th > Verb< / th >
< th > 50%< / th >
< th > 90%< / th >
< th > 99%< / th >
< / tr >
< / thead >
< tbody >
< tr >
< td > services< / td >
< td > DELETE< / td >
< td > 8.472ms< / td >
< td > 9.841ms< / td >
< td > 38.226ms< / td >
< / tr >
< tr >
< td > endpoints< / td >
< td > PUT< / td >
< td > 1.641ms< / td >
< td > 3.161ms< / td >
< td > 30.715ms< / td >
< / tr >
< tr >
< td > endpoints< / td >
< td > GET< / td >
< td > 931µ s< / td >
< td > 10.412ms< / td >
< td > 27.97ms< / td >
< / tr >
< tr >
< td > nodes< / td >
< td > PATCH< / td >
< td > 4.245ms< / td >
< td > 11.117ms< / td >
< td > 18.63ms< / td >
< / tr >
< tr >
< td > pods< / td >
< td > PUT< / td >
< td > 2.193ms< / td >
< td > 2.619ms< / td >
< td > 17.285ms< / td >
< / tr >
< / tbody >
< / table >
< p > 从 < code > log.txt< / code > 日 志 中 还 可 以 看 到 更 多 详 细 请 求 的 测 试 指 标 。 < / p >
2017-10-20 10:51:14 +08:00
< figure id = "fig1.5.2.3.1" > < img src = "http://olz1di9xf.bkt.clouddn.com/kubenetes-e2e-test.jpg" alt = "kubernetes-dashboard" > < figcaption > 图 片 - kubernetes-dashboard< / figcaption > < / figure >
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< h3 id = "注意事项" > 注 意 事 项 < / h3 >
< p > 测 试 过 程 中 需 要 用 到 docker镜 像 存 储 在 GCE中 ， 需 要 翻 墙 下 载 ， 我 没 看 到 哪 里 配 置 这 个 镜 像 的 地 址 。 该 镜 像 副 本 已 上 传 时 速 云 ： < / p >
< p > 用 到 的 镜 像 有 如 下 两 个 ： < / p >
< ul >
< li > gcr.io/google_containers/pause-amd64:3.0< / li >
< li > gcr.io/google_containers/serve_hostname:v1.4< / li >
< / ul >
< p > 时 速 云 镜 像 地 址 ： < / p >
< ul >
< li > index.tenxcloud.com/jimmy/pause-amd64:3.0< / li >
< li > index.tenxcloud.com/jimmy/serve_hostname:v1.4< / li >
< / ul >
< p > 将 镜 像 pull到 本 地 后 重 新 打 tag。 < / p >
< h2 id = "locust测试" > Locust测 试 < / h2 >
< p > 请 求 统 计 < / p >
< table >
< thead >
< tr >
< th > Method< / th >
< th > Name< / th >
< th > # requests< / th >
< th > # failures< / th >
< th > Median response time< / th >
< th > Average response time< / th >
< th > Min response time< / th >
< th > Max response time< / th >
< th > Average Content Size< / th >
< th > Requests/s< / th >
< / tr >
< / thead >
< tbody >
< tr >
< td > POST< / td >
< td > /login< / td >
< td > 5070< / td >
< td > 78< / td >
< td > 59000< / td >
< td > 80551< / td >
< td > 11218< / td >
< td > 202140< / td >
< td > 54< / td >
< td > 1.17< / td >
< / tr >
< tr >
< td > POST< / td >
< td > /metrics< / td >
< td > 5114232< / td >
< td > 85879< / td >
< td > 63000< / td >
< td > 82280< / td >
< td > 29518< / td >
< td > 331330< / td >
< td > 94< / td >
< td > 1178.77< / td >
< / tr >
< tr >
< td > None< / td >
< td > Total< / td >
< td > 5119302< / td >
< td > 85957< / td >
< td > 63000< / td >
< td > 82279< / td >
< td > 11218< / td >
< td > 331330< / td >
< td > 94< / td >
< td > 1179.94< / td >
< / tr >
< / tbody >
< / table >
< p > 响 应 时 间 分 布 < / p >
< table >
< thead >
< tr >
< th > Name< / th >
< th > # requests< / th >
< th > 50%< / th >
< th > 66%< / th >
< th > 75%< / th >
< th > 80%< / th >
< th > 90%< / th >
< th > 95%< / th >
< th > 98%< / th >
< th > 99%< / th >
< th > 100%< / th >
< / tr >
< / thead >
< tbody >
< tr >
< td > POST /login< / td >
< td > 5070< / td >
< td > 59000< / td >
< td > 125000< / td >
< td > 140000< / td >
< td > 148000< / td >
< td > 160000< / td >
< td > 166000< / td >
< td > 174000< / td >
< td > 176000< / td >
< td > 202140< / td >
< / tr >
< tr >
< td > POST /metrics< / td >
< td > 5114993< / td >
< td > 63000< / td >
< td > 127000< / td >
< td > 142000< / td >
< td > 149000< / td >
< td > 160000< / td >
< td > 166000< / td >
< td > 172000< / td >
< td > 176000< / td >
< td > 331330< / td >
< / tr >
< tr >
< td > None Total< / td >
< td > 5120063< / td >
< td > 63000< / td >
< td > 127000< / td >
< td > 142000< / td >
< td > 149000< / td >
< td > 160000< / td >
< td > 166000< / td >
< td > 172000< / td >
< td > 176000< / td >
< td > 331330< / td >
< / tr >
< / tbody >
< / table >
< p > 以 上 两 个 表 格 都 是 瞬 时 值 。 请 求 失 败 率 在 2%左 右 。 < / p >
< p > Sample-webapp起 了 48个 pod。 < / p >
< p > Locust模 拟 10万 用 户 ， 每 秒 增 长 100个 。 < / p >
2017-10-20 10:51:14 +08:00
< figure id = "fig1.5.2.3.2" > < img src = "http://olz1di9xf.bkt.clouddn.com/kubernetes-locust-test.jpg" alt = "locust-test" > < figcaption > 图 片 - locust-test< / figcaption > < / figure >
2017-08-21 17:44:43 +08:00
< p > 关 于 Locust的 使 用 请 参 考 Github： < a href = "https://github.com/rootsongjc/distributed-load-testing-using-kubernetes" target = "_blank" > https://github.com/rootsongjc/distributed-load-testing-using-kubernetes< / a > < / p >
< h2 id = "参考" > 参 考 < / h2 >
< p > < a href = "https://testerhome.com/topics/4839" target = "_blank" > 基 于 Python 的 性 能 测 试 工 具 locust (与 LR 的 简 单 对 比 )< / a > < / p >
< p > < a href = "http://docs.locust.io/en/latest/what-is-locust.html" target = "_blank" > Locust docs< / a > < / p >
< p > < a href = "http://timd.cn/2015/09/17/locust/" target = "_blank" > python用 户 负 载 测 试 工 具 ： locust< / a > < / p >
< p > < a href = "https://supereagle.github.io/2017/03/09/kubemark/" target = "_blank" > Kubernetes集 群 性 能 测 试 < / a > < / p >
< p > < a href = "http://dockone.io/article/1050" target = "_blank" > CoreOS是 如 何 将 Kubernetes的 性 能 提 高 10倍 的 < / a > < / p >
< p > < a href = "http://blog.fleeto.us/translation/updates-performance-and-scalability-kubernetes-13-2000-node-60000-pod-clusters" target = "_blank" > Kubernetes 1.3 的 性 能 和 弹 性 — — 2000 节 点 ， 60,0000 Pod 的 集 群 < / a > < / p >
< p > < a href = "http://www.csdn.net/article/2015-07-07/2825155" target = "_blank" > 运 用 Kubernetes进 行 分 布 式 负 载 测 试 < / a > < / p >
< p > < a href = "https://github.com/kubernetes/community/blob/master/contributors/devel/kubemark-guide.md" target = "_blank" > Kubemark User Guide< / a > < / p >
2017-10-09 15:28:05 +08:00
< p > < a href = "https://docs.openshift.com/container-platform/3.4/architecture/additional_concepts/flannel.html" target = "_blank" > Flannel host-gw architecture< / a > < / p >
< footer class = "page-footer" > < span class = "copyright" > Copyright © jimmysong.io 2017 all right reserved， powered by Gitbook< / span > < span class = "footer-modification" > Updated:
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