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1. 前言
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2.1 设计理念
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2.2 Objects
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<a href="../concepts/pod-overview.html">
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2.2.1 Pod
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<ul class="articles">
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<li class="chapter " data-level="1.2.2.1.1" data-path="../concepts/pod.html">
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<a href="../concepts/pod.html">
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2.2.1.1 Pod解析
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</a>
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<a href="../concepts/init-containers.html">
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2.2.1.2 Init容器
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</a>
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<li class="chapter " data-level="1.2.2.1.3" data-path="../concepts/pod-security-policy.html">
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<a href="../concepts/pod-security-policy.html">
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2.2.1.3 Pod安全策略
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</a>
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<li class="chapter " data-level="1.2.2.1.4" data-path="../concepts/pod-lifecycle.html">
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<a href="../concepts/pod-lifecycle.html">
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2.2.1.4 Pod的生命周期
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<a href="../concepts/node.html">
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2.2.2 Node
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</a>
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<li class="chapter " data-level="1.2.2.3" data-path="../concepts/namespace.html">
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<a href="../concepts/namespace.html">
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2.2.3 Namespace
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</a>
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<a href="../concepts/service.html">
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2.2.4 Service
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</a>
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<li class="chapter " data-level="1.2.2.5" data-path="../concepts/volume.html">
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<a href="../concepts/volume.html">
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2.2.5 Volume和Persistent Volume
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</a>
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<li class="chapter " data-level="1.2.2.6" data-path="../concepts/deployment.html">
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<a href="../concepts/deployment.html">
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2.2.6 Deployment
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<li class="chapter " data-level="1.2.2.7" data-path="../concepts/secret.html">
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<a href="../concepts/secret.html">
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2.2.7 Secret
|
||
|
||
</a>
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</li>
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<li class="chapter " data-level="1.2.2.8" data-path="../concepts/statefulset.html">
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<a href="../concepts/statefulset.html">
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||
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2.2.8 StatefulSet
|
||
|
||
</a>
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</li>
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<li class="chapter " data-level="1.2.2.9" data-path="../concepts/daemonset.html">
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<a href="../concepts/daemonset.html">
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2.2.9 DaemonSet
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||
</a>
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</li>
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<li class="chapter " data-level="1.2.2.10" data-path="../concepts/serviceaccount.html">
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<a href="../concepts/serviceaccount.html">
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2.2.10 ServiceAccount
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||
</a>
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</li>
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<li class="chapter " data-level="1.2.2.11" data-path="../concepts/replicaset.html">
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<a href="../concepts/replicaset.html">
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||
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||
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2.2.11 ReplicationController和ReplicaSet
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||
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||
</a>
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</li>
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<li class="chapter " data-level="1.2.2.12" data-path="../concepts/job.html">
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<a href="../concepts/job.html">
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2.2.12 Job
|
||
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||
</a>
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||
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</li>
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<li class="chapter " data-level="1.2.2.13" data-path="../concepts/cronjob.html">
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<a href="../concepts/cronjob.html">
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||
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||
|
||
2.2.13 CronJob
|
||
|
||
</a>
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||
|
||
|
||
|
||
</li>
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|
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<li class="chapter " data-level="1.2.2.14" data-path="../concepts/ingress.html">
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<a href="../concepts/ingress.html">
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2.2.14 Ingress
|
||
|
||
</a>
|
||
|
||
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||
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</li>
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|
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<li class="chapter " data-level="1.2.2.15" data-path="../concepts/configmap.html">
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<a href="../concepts/configmap.html">
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||
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2.2.15 ConfigMap
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
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||
|
||
<li class="chapter " data-level="1.2.2.16" data-path="../concepts/horizontal-pod-autoscaling.html">
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||
|
||
<a href="../concepts/horizontal-pod-autoscaling.html">
|
||
|
||
|
||
2.2.16 Horizontal Pod Autoscaling
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
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<li class="chapter " data-level="1.2.2.17" data-path="../concepts/label.html">
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<a href="../concepts/label.html">
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|
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2.2.17 Label
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
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|
||
<li class="chapter " data-level="1.2.2.18" data-path="../concepts/garbage-collection.html">
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||
|
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<a href="../concepts/garbage-collection.html">
|
||
|
||
|
||
2.2.18 垃圾收集
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.2.2.19" data-path="../concepts/network-policy.html">
|
||
|
||
<a href="../concepts/network-policy.html">
|
||
|
||
|
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2.2.19 NetworkPolicy
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
|
||
</ul>
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||
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</li>
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||
|
||
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||
</ul>
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</li>
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|
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<li class="chapter " data-level="1.3" data-path="../guide/">
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||
<a href="../guide/">
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||
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3. 用户指南
|
||
|
||
</a>
|
||
|
||
|
||
|
||
<ul class="articles">
|
||
|
||
|
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<li class="chapter " data-level="1.3.1" data-path="../guide/resource-configuration.html">
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|
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<a href="../guide/resource-configuration.html">
|
||
|
||
|
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3.1 资源对象配置
|
||
|
||
</a>
|
||
|
||
|
||
|
||
<ul class="articles">
|
||
|
||
|
||
<li class="chapter " data-level="1.3.1.1" data-path="../guide/configure-liveness-readiness-probes.html">
|
||
|
||
<a href="../guide/configure-liveness-readiness-probes.html">
|
||
|
||
|
||
3.1.1 配置Pod的liveness和readiness探针
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.3.1.2" data-path="../guide/configure-pod-service-account.html">
|
||
|
||
<a href="../guide/configure-pod-service-account.html">
|
||
|
||
|
||
3.1.2 配置Pod的Service Account
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.3.1.3" data-path="../guide/secret-configuration.html">
|
||
|
||
<a href="../guide/secret-configuration.html">
|
||
|
||
|
||
3.1.3 Secret配置
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.3.1.4" data-path="../guide/resource-quota-management.html">
|
||
|
||
<a href="../guide/resource-quota-management.html">
|
||
|
||
|
||
3.2.3 管理namespace中的资源配额
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
|
||
</ul>
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.3.2" data-path="../guide/command-usage.html">
|
||
|
||
<a href="../guide/command-usage.html">
|
||
|
||
|
||
3.2 命令使用
|
||
|
||
</a>
|
||
|
||
|
||
|
||
<ul class="articles">
|
||
|
||
|
||
<li class="chapter " data-level="1.3.2.1" data-path="../guide/using-kubectl.html">
|
||
|
||
<a href="../guide/using-kubectl.html">
|
||
|
||
|
||
3.2.1 使用kubectl
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.3.2.2" data-path="../guide/docker-cli-to-kubectl.html">
|
||
|
||
<a href="../guide/docker-cli-to-kubectl.html">
|
||
|
||
|
||
3.2.2 docker用户过度到kubectl命令行指南
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
|
||
</ul>
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.3.3" data-path="../guide/cluster-security-management.html">
|
||
|
||
<a href="../guide/cluster-security-management.html">
|
||
|
||
|
||
3.3 集群安全性管理
|
||
|
||
</a>
|
||
|
||
|
||
|
||
<ul class="articles">
|
||
|
||
|
||
<li class="chapter " data-level="1.3.3.1" data-path="../guide/managing-tls-in-a-cluster.html">
|
||
|
||
<a href="../guide/managing-tls-in-a-cluster.html">
|
||
|
||
|
||
3.3.1 管理集群中的TLS
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.3.3.2" data-path="../guide/kubelet-authentication-authorization.html">
|
||
|
||
<a href="../guide/kubelet-authentication-authorization.html">
|
||
|
||
|
||
3.3.2 kubelet的认证授权
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.3.3.3" data-path="../guide/tls-bootstrapping.html">
|
||
|
||
<a href="../guide/tls-bootstrapping.html">
|
||
|
||
|
||
3.3.3 TLS bootstrap
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.3.3.4" data-path="../guide/kubectl-user-authentication-authorization.html">
|
||
|
||
<a href="../guide/kubectl-user-authentication-authorization.html">
|
||
|
||
|
||
3.3.4 kubectl的用户认证授权
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.3.3.5" data-path="../guide/rbac.html">
|
||
|
||
<a href="../guide/rbac.html">
|
||
|
||
|
||
3.3.5 RBAC——基于角色的访问控制
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.3.3.6" data-path="../guide/ip-masq-agent.html">
|
||
|
||
<a href="../guide/ip-masq-agent.html">
|
||
|
||
|
||
3.3.6 IP伪装代理
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
|
||
</ul>
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.3.4" data-path="../guide/access-kubernetes-cluster.html">
|
||
|
||
<a href="../guide/access-kubernetes-cluster.html">
|
||
|
||
|
||
3.4 访问 Kubernetes 集群
|
||
|
||
</a>
|
||
|
||
|
||
|
||
<ul class="articles">
|
||
|
||
|
||
<li class="chapter " data-level="1.3.4.1" data-path="../guide/access-cluster.html">
|
||
|
||
<a href="../guide/access-cluster.html">
|
||
|
||
|
||
3.4.1 访问集群
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.3.4.2" data-path="../guide/authenticate-across-clusters-kubeconfig.html">
|
||
|
||
<a href="../guide/authenticate-across-clusters-kubeconfig.html">
|
||
|
||
|
||
3.4.2 使用 kubeconfig 文件配置跨集群认证
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.3.4.3" data-path="../guide/connecting-to-applications-port-forward.html">
|
||
|
||
<a href="../guide/connecting-to-applications-port-forward.html">
|
||
|
||
|
||
3.4.3 通过端口转发访问集群中的应用程序
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.3.4.4" data-path="../guide/service-access-application-cluster.html">
|
||
|
||
<a href="../guide/service-access-application-cluster.html">
|
||
|
||
|
||
3.4.4 使用 service 访问群集中的应用程序
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
|
||
</ul>
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.3.5" data-path="../guide/application-development-deployment-flow.html">
|
||
|
||
<a href="../guide/application-development-deployment-flow.html">
|
||
|
||
|
||
3.5 在kubernetes中开发部署应用
|
||
|
||
</a>
|
||
|
||
|
||
|
||
<ul class="articles">
|
||
|
||
|
||
<li class="chapter " data-level="1.3.5.1" data-path="../guide/deploy-applications-in-kubernetes.html">
|
||
|
||
<a href="../guide/deploy-applications-in-kubernetes.html">
|
||
|
||
|
||
3.5.1 适用于kubernetes的应用开发部署流程
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.3.5.2" data-path="../guide/migrating-hadoop-yarn-to-kubernetes.html">
|
||
|
||
<a href="../guide/migrating-hadoop-yarn-to-kubernetes.html">
|
||
|
||
|
||
3.5.2 迁移传统应用到kubernetes中——以Hadoop YARN为例
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
|
||
</ul>
|
||
|
||
</li>
|
||
|
||
|
||
</ul>
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.4" data-path="../practice/">
|
||
|
||
<a href="../practice/">
|
||
|
||
|
||
4. 最佳实践
|
||
|
||
</a>
|
||
|
||
|
||
|
||
<ul class="articles">
|
||
|
||
|
||
<li class="chapter " data-level="1.4.1" data-path="../practice/install-kbernetes1.6-on-centos.html">
|
||
|
||
<a href="../practice/install-kbernetes1.6-on-centos.html">
|
||
|
||
|
||
4.1 在CentOS上部署kubernetes1.6集群
|
||
|
||
</a>
|
||
|
||
|
||
|
||
<ul class="articles">
|
||
|
||
|
||
<li class="chapter " data-level="1.4.1.1" data-path="../practice/create-tls-and-secret-key.html">
|
||
|
||
<a href="../practice/create-tls-and-secret-key.html">
|
||
|
||
|
||
4.1.1 创建TLS证书和秘钥
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.4.1.2" data-path="../practice/create-kubeconfig.html">
|
||
|
||
<a href="../practice/create-kubeconfig.html">
|
||
|
||
|
||
4.1.2 创建kubeconfig文件
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.4.1.3" data-path="../practice/etcd-cluster-installation.html">
|
||
|
||
<a href="../practice/etcd-cluster-installation.html">
|
||
|
||
|
||
4.1.3 创建高可用etcd集群
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.4.1.4" data-path="../practice/kubectl-installation.html">
|
||
|
||
<a href="../practice/kubectl-installation.html">
|
||
|
||
|
||
4.1.4 安装kubectl命令行工具
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.4.1.5" data-path="../practice/master-installation.html">
|
||
|
||
<a href="../practice/master-installation.html">
|
||
|
||
|
||
4.1.5 部署master节点
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.4.1.6" data-path="../practice/node-installation.html">
|
||
|
||
<a href="../practice/node-installation.html">
|
||
|
||
|
||
4.1.6 部署node节点
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.4.1.7" data-path="../practice/kubedns-addon-installation.html">
|
||
|
||
<a href="../practice/kubedns-addon-installation.html">
|
||
|
||
|
||
4.1.7 安装kubedns插件
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.4.1.8" data-path="../practice/dashboard-addon-installation.html">
|
||
|
||
<a href="../practice/dashboard-addon-installation.html">
|
||
|
||
|
||
4.1.8 安装dashboard插件
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.4.1.9" data-path="../practice/heapster-addon-installation.html">
|
||
|
||
<a href="../practice/heapster-addon-installation.html">
|
||
|
||
|
||
4.1.9 安装heapster插件
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.4.1.10" data-path="../practice/efk-addon-installation.html">
|
||
|
||
<a href="../practice/efk-addon-installation.html">
|
||
|
||
|
||
4.1.10 安装EFK插件
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
|
||
</ul>
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.4.2" data-path="../practice/service-discovery-and-loadbalancing.html">
|
||
|
||
<a href="../practice/service-discovery-and-loadbalancing.html">
|
||
|
||
|
||
4.2 服务发现与负载均衡
|
||
|
||
</a>
|
||
|
||
|
||
|
||
<ul class="articles">
|
||
|
||
|
||
<li class="chapter " data-level="1.4.2.1" data-path="../practice/traefik-ingress-installation.html">
|
||
|
||
<a href="../practice/traefik-ingress-installation.html">
|
||
|
||
|
||
4.2.1 安装Traefik ingress
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.4.2.2" data-path="../practice/distributed-load-test.html">
|
||
|
||
<a href="../practice/distributed-load-test.html">
|
||
|
||
|
||
4.2.2 分布式负载测试
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.4.2.3" data-path="../practice/network-and-cluster-perfermance-test.html">
|
||
|
||
<a href="../practice/network-and-cluster-perfermance-test.html">
|
||
|
||
|
||
4.2.3 网络和集群性能测试
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.4.2.4" data-path="../practice/edge-node-configuration.html">
|
||
|
||
<a href="../practice/edge-node-configuration.html">
|
||
|
||
|
||
4.2.4 边缘节点配置
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
|
||
</ul>
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.4.3" data-path="../practice/operation.html">
|
||
|
||
<a href="../practice/operation.html">
|
||
|
||
|
||
4.3 运维管理
|
||
|
||
</a>
|
||
|
||
|
||
|
||
<ul class="articles">
|
||
|
||
|
||
<li class="chapter " data-level="1.4.3.1" data-path="../practice/service-rolling-update.html">
|
||
|
||
<a href="../practice/service-rolling-update.html">
|
||
|
||
|
||
4.3.1 服务滚动升级
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.4.3.2" data-path="../practice/app-log-collection.html">
|
||
|
||
<a href="../practice/app-log-collection.html">
|
||
|
||
|
||
4.3.2 应用日志收集
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.4.3.3" data-path="../practice/configuration-best-practice.html">
|
||
|
||
<a href="../practice/configuration-best-practice.html">
|
||
|
||
|
||
4.3.3 配置最佳实践
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.4.3.4" data-path="../practice/monitor.html">
|
||
|
||
<a href="../practice/monitor.html">
|
||
|
||
|
||
4.3.4 集群及应用监控
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.4.3.5" data-path="../practice/jenkins-ci-cd.html">
|
||
|
||
<a href="../practice/jenkins-ci-cd.html">
|
||
|
||
|
||
4.3.5 使用Jenkins进行持续构建与发布
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.4.3.6" data-path="../practice/data-persistence-problem.html">
|
||
|
||
<a href="../practice/data-persistence-problem.html">
|
||
|
||
|
||
4.3.6 数据持久化问题
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.4.3.7" data-path="../practice/manage-compute-resources-container.html">
|
||
|
||
<a href="../practice/manage-compute-resources-container.html">
|
||
|
||
|
||
4.3.7 管理容器的计算资源
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.4.3.8" data-path="../practice/using-prometheus-to-monitor-kuberentes-cluster.html">
|
||
|
||
<a href="../practice/using-prometheus-to-monitor-kuberentes-cluster.html">
|
||
|
||
|
||
4.3.8 使用Prometheus监控kubernetes集群
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.4.3.9" data-path="../practice/using-heapster-to-get-object-metrics.html">
|
||
|
||
<a href="../practice/using-heapster-to-get-object-metrics.html">
|
||
|
||
|
||
4.3.9 使用Heapster获取集群和对象的metric数据
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
|
||
</ul>
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.4.4" data-path="../practice/storage.html">
|
||
|
||
<a href="../practice/storage.html">
|
||
|
||
|
||
4.4 存储管理
|
||
|
||
</a>
|
||
|
||
|
||
|
||
<ul class="articles">
|
||
|
||
|
||
<li class="chapter " data-level="1.4.4.1" data-path="../practice/glusterfs.html">
|
||
|
||
<a href="../practice/glusterfs.html">
|
||
|
||
|
||
4.4.1 GlusterFS
|
||
|
||
</a>
|
||
|
||
|
||
|
||
<ul class="articles">
|
||
|
||
|
||
<li class="chapter " data-level="1.4.4.1.1" data-path="../practice/using-glusterfs-for-persistent-storage.html">
|
||
|
||
<a href="../practice/using-glusterfs-for-persistent-storage.html">
|
||
|
||
|
||
4.4.1.1 使用GlusterFS做持久化存储
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.4.4.1.2" data-path="../practice/storage-for-containers-using-glusterfs-with-openshift.html">
|
||
|
||
<a href="../practice/storage-for-containers-using-glusterfs-with-openshift.html">
|
||
|
||
|
||
4.4.1.2 在OpenShift中使用GlusterFS做持久化存储
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
|
||
</ul>
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.4.4.2" data-path="../practice/cephfs.html">
|
||
|
||
<a href="../practice/cephfs.html">
|
||
|
||
|
||
4.4.2 CephFS
|
||
|
||
</a>
|
||
|
||
|
||
|
||
<ul class="articles">
|
||
|
||
|
||
<li class="chapter " data-level="1.4.4.2.1" data-path="../practice/using-ceph-for-persistent-storage.html">
|
||
|
||
<a href="../practice/using-ceph-for-persistent-storage.html">
|
||
|
||
|
||
4.4.2.1 使用Ceph做持久化存储
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
|
||
</ul>
|
||
|
||
</li>
|
||
|
||
|
||
</ul>
|
||
|
||
</li>
|
||
|
||
|
||
</ul>
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.5" data-path="./">
|
||
|
||
<a href="./">
|
||
|
||
|
||
5. 领域应用
|
||
|
||
</a>
|
||
|
||
|
||
|
||
<ul class="articles">
|
||
|
||
|
||
<li class="chapter " data-level="1.5.1" data-path="microservices.html">
|
||
|
||
<a href="microservices.html">
|
||
|
||
|
||
5.1 微服务架构
|
||
|
||
</a>
|
||
|
||
|
||
|
||
<ul class="articles">
|
||
|
||
|
||
<li class="chapter " data-level="1.5.1.1" data-path="service-discovery-in-microservices.html">
|
||
|
||
<a href="service-discovery-in-microservices.html">
|
||
|
||
|
||
5.1.1 微服务中的服务发现
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
|
||
</ul>
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.5.2" data-path="service-mesh.html">
|
||
|
||
<a href="service-mesh.html">
|
||
|
||
|
||
5.2 Service Mesh 服务网格
|
||
|
||
</a>
|
||
|
||
|
||
|
||
<ul class="articles">
|
||
|
||
|
||
<li class="chapter " data-level="1.5.2.1" data-path="istio.html">
|
||
|
||
<a href="istio.html">
|
||
|
||
|
||
5.1.1 Istio
|
||
|
||
</a>
|
||
|
||
|
||
|
||
<ul class="articles">
|
||
|
||
|
||
<li class="chapter " data-level="1.5.2.1.1" data-path="istio-installation.html">
|
||
|
||
<a href="istio-installation.html">
|
||
|
||
|
||
5.1.1.1 安装istio
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.5.2.1.2" data-path="configuring-request-routing.html">
|
||
|
||
<a href="configuring-request-routing.html">
|
||
|
||
|
||
5.1.1.2 配置请求的路由规则
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
|
||
</ul>
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.5.2.2" data-path="linkerd.html">
|
||
|
||
<a href="linkerd.html">
|
||
|
||
|
||
5.1.2 Linkerd
|
||
|
||
</a>
|
||
|
||
|
||
|
||
<ul class="articles">
|
||
|
||
|
||
<li class="chapter " data-level="1.5.2.2.1" data-path="linkerd-user-guide.html">
|
||
|
||
<a href="linkerd-user-guide.html">
|
||
|
||
|
||
5.1.2.1 Linkerd 使用指南
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
|
||
</ul>
|
||
|
||
</li>
|
||
|
||
|
||
</ul>
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.5.3" data-path="big-data.html">
|
||
|
||
<a href="big-data.html">
|
||
|
||
|
||
5.2 大数据
|
||
|
||
</a>
|
||
|
||
|
||
|
||
<ul class="articles">
|
||
|
||
|
||
<li class="chapter " data-level="1.5.3.1" data-path="spark-standalone-on-kubernetes.html">
|
||
|
||
<a href="spark-standalone-on-kubernetes.html">
|
||
|
||
|
||
5.2.1 Spark standalone on Kubernetes
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter active" data-level="1.5.3.2" data-path="running-spark-with-kubernetes-native-scheduler.html">
|
||
|
||
<a href="running-spark-with-kubernetes-native-scheduler.html">
|
||
|
||
|
||
5.2.2 运行支持kubernetes原生调度的Spark程序
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
|
||
</ul>
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.5.4" data-path="serverless.html">
|
||
|
||
<a href="serverless.html">
|
||
|
||
|
||
5.3 Serverless架构
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
|
||
</ul>
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.6" data-path="../develop/">
|
||
|
||
<a href="../develop/">
|
||
|
||
|
||
6. 开发指南
|
||
|
||
</a>
|
||
|
||
|
||
|
||
<ul class="articles">
|
||
|
||
|
||
<li class="chapter " data-level="1.6.1" data-path="../develop/developing-environment.html">
|
||
|
||
<a href="../develop/developing-environment.html">
|
||
|
||
|
||
6.1 开发环境搭建
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.6.2" data-path="../develop/testing.html">
|
||
|
||
<a href="../develop/testing.html">
|
||
|
||
|
||
6.2 单元测试和集成测试
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.6.3" data-path="../develop/client-go-sample.html">
|
||
|
||
<a href="../develop/client-go-sample.html">
|
||
|
||
|
||
6.3 client-go示例
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.6.4" data-path="../develop/contribute.html">
|
||
|
||
<a href="../develop/contribute.html">
|
||
|
||
|
||
6.4 社区贡献
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
|
||
</ul>
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.7" data-path="../appendix/">
|
||
|
||
<a href="../appendix/">
|
||
|
||
|
||
7. 附录
|
||
|
||
</a>
|
||
|
||
|
||
|
||
<ul class="articles">
|
||
|
||
|
||
<li class="chapter " data-level="1.7.1" data-path="../appendix/docker-best-practice.html">
|
||
|
||
<a href="../appendix/docker-best-practice.html">
|
||
|
||
|
||
7.1 Docker最佳实践
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.7.2" data-path="../appendix/issues.html">
|
||
|
||
<a href="../appendix/issues.html">
|
||
|
||
|
||
7.2 问题记录
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.7.3" data-path="../appendix/tricks.html">
|
||
|
||
<a href="../appendix/tricks.html">
|
||
|
||
|
||
7.3 使用技巧
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.7.4" data-path="../appendix/debug-kubernetes-services.html">
|
||
|
||
<a href="../appendix/debug-kubernetes-services.html">
|
||
|
||
|
||
7.4 kubernetes service中的故障排查
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
<li class="chapter " data-level="1.7.5" data-path="../appendix/material-share.html">
|
||
|
||
<a href="../appendix/material-share.html">
|
||
|
||
|
||
7.5 相关资料分享
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
|
||
</ul>
|
||
|
||
</li>
|
||
|
||
|
||
|
||
|
||
<li class="divider"></li>
|
||
|
||
<li>
|
||
<a href="https://www.gitbook.com" target="blank" class="gitbook-link">
|
||
本书使用 GitBook 发布
|
||
</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=".." >5.2.2 运行支持kubernetes原生调度的Spark程序</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="运行支持kubernetes原生调度的spark程序">运行支持kubernetes原生调度的Spark程序</h1>
|
||
<p>TL;DR 这个主题比较大，该开源项目也还在不断进行中，我单独做了一个 web 用来记录 spark on kubernetes 的研究和最新进展见: <a href="https://jimmysong.io/spark-on-k8s" target="_blank">https://jimmysong.io/spark-on-k8s</a></p>
|
||
<p>我们之前就在 kubernetes 中运行过 standalone 方式的 spark 集群，见 <a href="spark-standalone-on-kubernetes.html">Spark standalone on kubernetes</a>。</p>
|
||
<p>目前运行支持 kubernetes 原生调度的 spark 程序由 Google 主导，目前运行支持 kubernetes 原生调度的 spark 程序由 Google 主导，fork 自 spark 的官方代码库，见<a href="https://github.com/apache-spark-on-k8s/spark/" target="_blank">https://github.com/apache-spark-on-k8s/spark/</a> ，属于Big Data SIG。</p>
|
||
<p>参与到该项目的公司有：</p>
|
||
<ul>
|
||
<li>Bloomberg</li>
|
||
<li>Google</li>
|
||
<li>Haiwen</li>
|
||
<li>Hyperpilot</li>
|
||
<li>Intel</li>
|
||
<li>Palantir</li>
|
||
<li>Pepperdata</li>
|
||
<li>Red Hat</li>
|
||
</ul>
|
||
<h2 id="为何使用-spark-on-kubernetes">为何使用 spark on kubernetes</h2>
|
||
<p>使用kubernetes原生调度的spark on kubernetes是对现有的spark on yarn/mesos的资源使用方式的革命性的改进，主要表现在以下几点：</p>
|
||
<ol>
|
||
<li>Kubernetes原生调度：不再需要二层调度，直接使用kubernetes的资源调度功能，跟其他应用共用整个kubernetes管理的资源池；</li>
|
||
<li>资源隔离，粒度更细：原先yarn中的queue在spark on kubernetes中已不存在，取而代之的是kubernetes中原生的namespace，可以为每个用户分别指定一个namespace，限制用户的资源quota；</li>
|
||
<li>细粒度的资源分配：可以给每个spark任务指定资源限制，实际指定多少资源就使用多少资源，因为没有了像yarn那样的二层调度（圈地式的），所以可以更高效和细粒度的使用资源；</li>
|
||
<li>监控的变革：因为做到了细粒度的资源分配，所以可以对用户提交的每一个任务做到资源使用的监控，从而判断用户的资源使用情况，所有的metric都记录在数据库中，甚至可以为每个用户的每次任务提交计量；</li>
|
||
<li>日志的变革：用户不再通过yarn的web页面来查看任务状态，而是通过pod的log来查看，可将所有的kuberentes中的应用的日志等同看待收集起来，然后可以根据标签查看对应应用的日志；</li>
|
||
</ol>
|
||
<p>所有这些变革都可以让我们更高效的获取资源、更有效率的获取资源！</p>
|
||
<h2 id="spark-概念说明">Spark 概念说明</h2>
|
||
<p><a href="http://spark.apache.org" target="_blank">Apache Spark</a> 是一个围绕速度、易用性和复杂分析构建的大数据处理框架。最初在2009年由加州大学伯克利分校的AMPLab开发，并于2010年成为Apache的开源项目之一。</p>
|
||
<p>在 Spark 中包括如下组件或概念：</p>
|
||
<ul>
|
||
<li><strong>Application</strong>：Spark Application 的概念和 Hadoop 中的 MapReduce 类似，指的是用户编写的 Spark 应用程序，包含了一个 Driver 功能的代码和分布在集群中多个节点上运行的 Executor 代码；</li>
|
||
<li><strong>Driver</strong>：Spark 中的 Driver 即运行上述 Application 的 main() 函数并且创建 SparkContext，其中创建 SparkContext 的目的是为了准备Spark应用程序的运行环境。在 Spark 中由 SparkContext 负责和 ClusterManager 通信，进行资源的申请、任务的分配和监控等；当 Executor 部分运行完毕后，Driver负责将SparkContext 关闭。通常用 SparkContext 代表 Driver；</li>
|
||
<li><strong>Executor</strong>：Application运行在Worker 节点上的一个进程，该进程负责运行Task，并且负责将数据存在内存或者磁盘上，每个Application都有各自独立的一批Executor。在Spark on Yarn模式下，其进程名称为<code>CoarseGrainedExecutorBackend</code>，类似于 Hadoop MapReduce 中的 YarnChild。一个 <code>CoarseGrainedExecutorBackend</code> 进程有且仅有一个 executor 对象，它负责将 Task 包装成 taskRunner，并从线程池中抽取出一个空闲线程运行 Task。每个 <code>CoarseGrainedExecutorBackend</code> 能并行运行 Task 的数量就取决于分配给它的 CPU 的个数了；</li>
|
||
<li><strong>Cluster Manager</strong>：指的是在集群上获取资源的外部服务，目前有：<ul>
|
||
<li>Standalone：Spark原生的资源管理，由Master负责资源的分配；</li>
|
||
<li>Hadoop Yarn：由YARN中的ResourceManager负责资源的分配；</li>
|
||
</ul>
|
||
</li>
|
||
<li><strong>Worker</strong>：集群中任何可以运行Application代码的节点，类似于YARN中的NodeManager节点。在Standalone模式中指的就是通过Slave文件配置的Worker节点，在Spark on Yarn模式中指的就是NodeManager节点；</li>
|
||
<li><strong>作业（Job）</strong>：包含多个Task组成的并行计算，往往由Spark Action催生，一个JOB包含多个RDD及作用于相应RDD上的各种Operation；</li>
|
||
<li><strong>阶段（Stage）</strong>：每个Job会被拆分很多组 Task，每组任务被称为Stage，也可称TaskSet，一个作业分为多个阶段，每一个stage的分割点是action。比如一个job是：（transformation1 -> transformation1 -> action1 -> transformation3 -> action2），这个job就会被分为两个stage，分割点是action1和action2。</li>
|
||
<li><p><strong>任务（Task）</strong>： 被送到某个Executor上的工作任务；</p>
|
||
</li>
|
||
<li><p><strong>Context</strong>：启动spark application的时候创建，作为Spark 运行时环境。</p>
|
||
</li>
|
||
<li><strong>Dynamic Allocation（动态资源分配）</strong>：一个配置选项，可以将其打开。从Spark1.2之后，对于On Yarn模式，已经支持动态资源分配（Dynamic Resource Allocation），这样，就可以根据Application的负载（Task情况），动态的增加和减少executors，这种策略非常适合在YARN上使用spark-sql做数据开发和分析，以及将spark-sql作为长服务来使用的场景。Executor 的动态分配需要在 cluster mode 下启用 "external shuffle service"。</li>
|
||
<li><strong>动态资源分配策略</strong>：开启动态分配策略后，application会在task因没有足够资源被挂起的时候去动态申请资源，这意味着该application现有的executor无法满足所有task并行运行。spark一轮一轮的申请资源，当有task挂起或等待 <code>spark.dynamicAllocation.schedulerBacklogTimeout</code> (默认1s)时间的时候，会开始动态资源分配；之后会每隔 <code>spark.dynamicAllocation.sustainedSchedulerBacklogTimeout</code> (默认1s)时间申请一次，直到申请到足够的资源。每次申请的资源量是指数增长的，即1,2,4,8等。之所以采用指数增长，出于两方面考虑：其一，开始申请的少是考虑到可能application会马上得到满足；其次要成倍增加，是为了防止application需要很多资源，而该方式可以在很少次数的申请之后得到满足。</li>
|
||
</ul>
|
||
<h2 id="架构设计">架构设计</h2>
|
||
<p>关于 spark standalone 的局限性与 kubernetes native spark 架构之间的区别请参考 Anirudh Ramanathan 在 2016年10月8日提交的 issue <a href="https://github.com/kubernetes/kubernetes/issues/34377" target="_blank">Support Spark natively in Kubernetes #34377</a>。</p>
|
||
<p>简而言之，spark standalone on kubernetes 有如下几个缺点：</p>
|
||
<ul>
|
||
<li>无法对于多租户做隔离，每个用户都想给 pod 申请 node 节点可用的最大的资源。</li>
|
||
<li>Spark 的 master／worker 本来不是设计成使用 kubernetes 的资源调度，这样会存在两层的资源调度问题，不利于与 kuberentes 集成。</li>
|
||
</ul>
|
||
<p>而 kubernetes native spark 集群中，spark 可以调用 kubernetes API 获取集群资源和调度。要实现 kubernetes native spark 需要为 spark 提供一个集群外部的 manager 可以用来跟 kubernetes API 交互。</p>
|
||
<h3 id="调度器后台">调度器后台</h3>
|
||
<p>使用 kubernetes 原生调度的 spark 的基本设计思路是将 spark 的 driver 和 executor 都放在 kubernetes 的 pod 中运行，另外还有两个附加的组件：<code>ResourceStagingServer</code> 和 <code>KubernetesExternalShuffleService</code>。</p>
|
||
<p>Spark driver 其实可以运行在 kubernetes 集群内部（cluster mode）可以运行在外部（client mode），executor 只能运行在集群内部，当有 spark 作业提交到 kubernetes 集群上时，调度器后台将会为 executor pod 设置如下属性：</p>
|
||
<ul>
|
||
<li>使用我们预先编译好的包含 kubernetes 支持的 spark 镜像，然后调用 <code>CoarseGrainedExecutorBackend</code> main class 启动 JVM。</li>
|
||
<li>调度器后台为 executor pod 的运行时注入环境变量，例如各种 JVM 参数，包括用户在 <code>spark-submit</code> 时指定的那些参数。</li>
|
||
<li>Executor 的 CPU、内存限制根据这些注入的环境变量保存到应用程序的 <code>SparkConf</code> 中。</li>
|
||
<li>可以在配置中指定 spark 运行在指定的 namespace 中。</li>
|
||
</ul>
|
||
<p>参考：<a href="https://github.com/apache-spark-on-k8s/spark/blob/branch-2.2-kubernetes/resource-managers/kubernetes/architecture-docs/scheduler-backend.md" target="_blank">Scheduler backend 文档</a></p>
|
||
<h2 id="安装指南">安装指南</h2>
|
||
<p>我们可以直接使用官方已编译好的 docker 镜像来部署，下面是官方发布的镜像：</p>
|
||
<table>
|
||
<thead>
|
||
<tr>
|
||
<th>组件</th>
|
||
<th>镜像</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr>
|
||
<td>Spark Driver Image</td>
|
||
<td><code>kubespark/spark-driver:v2.1.0-kubernetes-0.3.1</code></td>
|
||
</tr>
|
||
<tr>
|
||
<td>Spark Executor Image</td>
|
||
<td><code>kubespark/spark-executor:v2.1.0-kubernetes-0.3.1</code></td>
|
||
</tr>
|
||
<tr>
|
||
<td>Spark Initialization Image</td>
|
||
<td><code>kubespark/spark-init:v2.1.0-kubernetes-0.3.1</code></td>
|
||
</tr>
|
||
<tr>
|
||
<td>Spark Staging Server Image</td>
|
||
<td><code>kubespark/spark-resource-staging-server:v2.1.0-kubernetes-0.3.1</code></td>
|
||
</tr>
|
||
<tr>
|
||
<td>PySpark Driver Image</td>
|
||
<td><code>kubespark/driver-py:v2.1.0-kubernetes-0.3.1</code></td>
|
||
</tr>
|
||
<tr>
|
||
<td>PySpark Executor Image</td>
|
||
<td><code>kubespark/executor-py:v2.1.0-kubernetes-0.3.1</code></td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<p>我将这些镜像放到了我的私有镜像仓库中了。</p>
|
||
<p>还需要安装支持 kubernetes 的 spark 客户端，在这里下载：<a href="https://github.com/apache-spark-on-k8s/spark/releases" target="_blank">https://github.com/apache-spark-on-k8s/spark/releases</a></p>
|
||
<p>根据使用的镜像版本，我下载的是 <a href="https://github.com/apache-spark-on-k8s/spark/releases/tag/v2.1.0-kubernetes-0.3.1" target="_blank">v2.1.0-kubernetes-0.3.1</a> </p>
|
||
<p><strong>运行 SparkPi 测试</strong></p>
|
||
<p>我们将任务运行在 <code>spark-cluster</code> 的 namespace 中，启动 5 个 executor 实例。</p>
|
||
<pre><code class="lang-bash">./bin/spark-submit \
|
||
--deploy-mode cluster \
|
||
--class org.apache.spark.examples.SparkPi \
|
||
--master k8s://https://172.20.0.113:6443 \
|
||
--kubernetes-namespace spark-cluster \
|
||
--conf spark.executor.instances=5 \
|
||
--conf spark.app.name=spark-pi \
|
||
--conf spark.kubernetes.driver.docker.image=sz-pg-oam-docker-hub-001.tendcloud.com/library/kubespark-spark-driver:v2.1.0-kubernetes-0.3.1 \
|
||
--conf spark.kubernetes.executor.docker.image=sz-pg-oam-docker-hub-001.tendcloud.com/library/kubespark-spark-executor:v2.1.0-kubernetes-0.3.1 \
|
||
--conf spark.kubernetes.initcontainer.docker.image=sz-pg-oam-docker-hub-001.tendcloud.com/library/kubespark-spark-init:v2.1.0-kubernetes-0.3.1 \
|
||
<span class="hljs-built_in">local</span>:///opt/spark/examples/jars/spark-examples_2.11-2.1.0-k8s-0.3.1-SNAPSHOT.jar
|
||
</code></pre>
|
||
<p>关于该命令参数的介绍请参考：<a href="https://apache-spark-on-k8s.github.io/userdocs/running-on-kubernetes.html" target="_blank">https://apache-spark-on-k8s.github.io/userdocs/running-on-kubernetes.html</a></p>
|
||
<p><strong>注意：</strong> 该 jar 包实际上是 <code>spark.kubernetes.executor.docker.image</code> 镜像中的。</p>
|
||
<p>这时候提交任务运行还是失败，报错信息中可以看到两个问题：</p>
|
||
<ul>
|
||
<li>Executor 无法找到 driver pod</li>
|
||
<li>用户 <code>system:serviceaccount:spark-cluster:defaul</code> 没有权限获取 <code>spark-cluster</code> 中的 pod 信息。</li>
|
||
</ul>
|
||
<p>提了个 issue <a href="https://github.com/apache-spark-on-k8s/spark/issues/478" target="_blank">Failed to run the sample spark-pi test using spark-submit on the doc #478</a> </p>
|
||
<p>需要为 spark 集群创建一个 <code>serviceaccount</code> 和 <code>clusterrolebinding</code>：</p>
|
||
<pre><code class="lang-bash">kubectl create serviceaccount spark --namespace spark-cluster
|
||
kubectl create rolebinding spark-edit --clusterrole=edit --serviceaccount=spark-cluster:spark --namespace=spark-cluster
|
||
</code></pre>
|
||
<p>该 Bug 将在新版本中修复。</p>
|
||
<h2 id="用户指南">用户指南</h2>
|
||
<h3 id="编译">编译</h3>
|
||
<p>Fork 并克隆项目到本地：</p>
|
||
<pre><code class="lang-bash">git <span class="hljs-built_in">clone</span> https://github.com/rootsongjc/spark.git
|
||
</code></pre>
|
||
<p>编译前请确保你的环境中已经安装 Java8 和 Maven3。</p>
|
||
<pre><code class="lang-bash"><span class="hljs-comment">## 第一次编译前需要安装依赖</span>
|
||
build/mvn install -Pkubernetes -pl resource-managers/kubernetes/core -am -DskipTests
|
||
|
||
<span class="hljs-comment">## 编译 spark on kubernetes</span>
|
||
build/mvn compile -Pkubernetes -pl resource-managers/kubernetes/core -am -DskipTests
|
||
|
||
<span class="hljs-comment">## 发布</span>
|
||
dev/make-distribution.sh --tgz -Phadoop-2.7 -Pkubernetes
|
||
</code></pre>
|
||
<p>第一次编译和发布的过程耗时可能会比较长，请耐心等待，如果有依赖下载不下来，请自备梯子。</p>
|
||
<p>详细的开发指南请见：<a href="https://github.com/apache-spark-on-k8s/spark/blob/branch-2.2-kubernetes/resource-managers/kubernetes/README.md" target="_blank">https://github.com/apache-spark-on-k8s/spark/blob/branch-2.2-kubernetes/resource-managers/kubernetes/README.md</a></p>
|
||
<h3 id="构建镜像">构建镜像</h3>
|
||
<p>使用该脚本来自动构建容器镜像：<a href="https://github.com/apache-spark-on-k8s/spark/pull/488" target="_blank">https://github.com/apache-spark-on-k8s/spark/pull/488</a></p>
|
||
<p>将该脚本放在 <code>dist</code> 目录下，执行：</p>
|
||
<pre><code class="lang-bash">./build-push-docker-images.sh -r sz-pg-oam-docker-hub-001.tendcloud.com/library -t v2.1.0-kubernetes-0.3.1-1 build
|
||
./build-push-docker-images.sh -r sz-pg-oam-docker-hub-001.tendcloud.com/library -t v2.1.0-kubernetes-0.3.1-1 push
|
||
</code></pre>
|
||
<p><strong>注意：</strong>如果你使用的 MacOS，bash 的版本可能太低，执行改脚本将出错，请检查你的 bash 版本：</p>
|
||
<pre><code class="lang-bash">bash --version
|
||
GNU bash, version 3.2.57(1)-release (x86_64-apple-darwin16)
|
||
Copyright (C) 2007 Free Software Foundation, Inc.
|
||
</code></pre>
|
||
<p>上面我在升级 bash 之前获取的版本信息，使用下面的命令升级 bash：</p>
|
||
<pre><code class="lang-bash">brew install bash
|
||
</code></pre>
|
||
<p>升级后的 bash 版本为 <code>4.4.12(1)-release (x86_64-apple-darwin16.3.0)</code>。</p>
|
||
<p>编译并上传镜像到我的私有镜像仓库，将会构建出如下几个镜像：</p>
|
||
<pre><code class="lang-bash">sz-pg-oam-docker-hub-001.tendcloud.com/library/spark-driver:v2.1.0-kubernetes-0.3.1-1
|
||
sz-pg-oam-docker-hub-001.tendcloud.com/library/spark-resource-staging-server:v2.1.0-kubernetes-0.3.1-1
|
||
sz-pg-oam-docker-hub-001.tendcloud.com/library/spark-init:v2.1.0-kubernetes-0.3.1-1
|
||
sz-pg-oam-docker-hub-001.tendcloud.com/library/spark-shuffle:v2.1.0-kubernetes-0.3.1-1
|
||
sz-pg-oam-docker-hub-001.tendcloud.com/library/spark-executor:v2.1.0-kubernetes-0.3.1-1
|
||
sz-pg-oam-docker-hub-001.tendcloud.com/library/spark-executor-py:v2.1.0-kubernetes-0.3.1-1
|
||
sz-pg-oam-docker-hub-001.tendcloud.com/library/spark-driver-py:v2.1.0-kubernetes-0.3.1-1
|
||
</code></pre>
|
||
<h2 id="运行测试">运行测试</h2>
|
||
<p>在 <code>dist/bin</code> 目录下执行 spark-pi 测试：</p>
|
||
<pre><code class="lang-bash">./spark-submit \
|
||
--deploy-mode cluster \
|
||
--class org.apache.spark.examples.SparkPi \
|
||
--master k8s://https://172.20.0.113:6443 \
|
||
--kubernetes-namespace spark-cluster \
|
||
--conf spark.kubernetes.authenticate.driver.serviceAccountName=spark \
|
||
--conf spark.executor.instances=5 \
|
||
--conf spark.app.name=spark-pi \
|
||
--conf spark.kubernetes.driver.docker.image=sz-pg-oam-docker-hub-001.tendcloud.com/library/spark-driver:v2.1.0-kubernetes-0.3.1-1 \
|
||
--conf spark.kubernetes.executor.docker.image=sz-pg-oam-docker-hub-001.tendcloud.com/library/spark-executor:v2.1.0-kubernetes-0.3.1-1 \
|
||
--conf spark.kubernetes.initcontainer.docker.image=sz-pg-oam-docker-hub-001.tendcloud.com/library/spark-init:v2.1.0-kubernetes-0.3.1-1 \
|
||
<span class="hljs-built_in">local</span>:///opt/spark/examples/jars/spark-examples_2.11-2.2.0-k8s-0.4.0-SNAPSHOT.jar
|
||
</code></pre>
|
||
<p>详细的参数说明见：<a href="https://apache-spark-on-k8s.github.io/userdocs/running-on-kubernetes.html" target="_blank">https://apache-spark-on-k8s.github.io/userdocs/running-on-kubernetes.html</a></p>
|
||
<p><strong>注意：</strong><code>local:///opt/spark/examples/jars/spark-examples_2.11-2.2.0-k8s-0.4.0-SNAPSHOT.jar</code> 文件是在 <code>spark-driver</code> 和 <code>spark-executor</code> 镜像里的，在上一步构建镜像时已经构建并上传到了镜像仓库中。</p>
|
||
<p>执行日志显示：</p>
|
||
<pre><code class="lang-bash">2017-09-14 14:59:01 INFO Client:54 - Waiting <span class="hljs-keyword">for</span> application spark-pi to finish...
|
||
2017-09-14 14:59:01 INFO LoggingPodStatusWatcherImpl:54 - State changed, new state:
|
||
pod name: spark-pi-1505372339796-driver
|
||
namespace: spark-cluster
|
||
labels: spark-app-selector -> spark<span class="hljs-_">-f</span>4d3a5d3ad964a05a51feb6191d50357, spark-role -> driver
|
||
pod uid: 304cf440-991a-11e7-970c<span class="hljs-_">-f</span>4e9d49f8ed0
|
||
creation time: 2017-09-14T06:59:01Z
|
||
service account name: spark
|
||
volumes: spark-token-zr8wv
|
||
node name: N/A
|
||
start time: N/A
|
||
container images: N/A
|
||
phase: Pending
|
||
status: []
|
||
2017-09-14 14:59:01 INFO LoggingPodStatusWatcherImpl:54 - State changed, new state:
|
||
pod name: spark-pi-1505372339796-driver
|
||
namespace: spark-cluster
|
||
labels: spark-app-selector -> spark<span class="hljs-_">-f</span>4d3a5d3ad964a05a51feb6191d50357, spark-role -> driver
|
||
pod uid: 304cf440-991a-11e7-970c<span class="hljs-_">-f</span>4e9d49f8ed0
|
||
creation time: 2017-09-14T06:59:01Z
|
||
service account name: spark
|
||
volumes: spark-token-zr8wv
|
||
node name: 172.20.0.114
|
||
start time: N/A
|
||
container images: N/A
|
||
phase: Pending
|
||
status: []
|
||
2017-09-14 14:59:01 INFO LoggingPodStatusWatcherImpl:54 - State changed, new state:
|
||
pod name: spark-pi-1505372339796-driver
|
||
namespace: spark-cluster
|
||
labels: spark-app-selector -> spark<span class="hljs-_">-f</span>4d3a5d3ad964a05a51feb6191d50357, spark-role -> driver
|
||
pod uid: 304cf440-991a-11e7-970c<span class="hljs-_">-f</span>4e9d49f8ed0
|
||
creation time: 2017-09-14T06:59:01Z
|
||
service account name: spark
|
||
volumes: spark-token-zr8wv
|
||
node name: 172.20.0.114
|
||
start time: 2017-09-14T06:59:01Z
|
||
container images: sz-pg-oam-docker-hub-001.tendcloud.com/library/spark-driver:v2.1.0-kubernetes-0.3.1-1
|
||
phase: Pending
|
||
status: [ContainerStatus(containerID=null, image=sz-pg-oam-docker-hub-001.tendcloud.com/library/spark-driver:v2.1.0-kubernetes-0.3.1-1, imageID=, lastState=ContainerState(running=null, terminated=null, waiting=null, additionalProperties={}), name=spark-kubernetes-driver, ready=<span class="hljs-literal">false</span>, restartCount=0, state=ContainerState(running=null, terminated=null, waiting=ContainerStateWaiting(message=null, reason=ContainerCreating, additionalProperties={}), additionalProperties={}), additionalProperties={})]
|
||
2017-09-14 14:59:03 INFO LoggingPodStatusWatcherImpl:54 - State changed, new state:
|
||
pod name: spark-pi-1505372339796-driver
|
||
namespace: spark-cluster
|
||
labels: spark-app-selector -> spark<span class="hljs-_">-f</span>4d3a5d3ad964a05a51feb6191d50357, spark-role -> driver
|
||
pod uid: 304cf440-991a-11e7-970c<span class="hljs-_">-f</span>4e9d49f8ed0
|
||
creation time: 2017-09-14T06:59:01Z
|
||
service account name: spark
|
||
volumes: spark-token-zr8wv
|
||
node name: 172.20.0.114
|
||
start time: 2017-09-14T06:59:01Z
|
||
container images: sz-pg-oam-docker-hub-001.tendcloud.com/library/spark-driver:v2.1.0-kubernetes-0.3.1-1
|
||
phase: Running
|
||
status: [ContainerStatus(containerID=docker://5c5c821c482a1e35552adccb567020532b79244392374f25754f0050e6<span class="hljs-built_in">cd</span>4c62, image=sz-pg-oam-docker-hub-001.tendcloud.com/library/spark-driver:v2.1.0-kubernetes-0.3.1-1, imageID=docker-pullable://sz-pg-oam-docker-hub-001.tendcloud.com/library/spark-driver@sha256:beb92a3e3f178e286d9e5baebdead88b5ba76d651f347ad2864bb6f8eda26f94, lastState=ContainerState(running=null, terminated=null, waiting=null, additionalProperties={}), name=spark-kubernetes-driver, ready=<span class="hljs-literal">true</span>, restartCount=0, state=ContainerState(running=ContainerStateRunning(startedAt=2017-09-14T06:59:02Z, additionalProperties={}), terminated=null, waiting=null, additionalProperties={}), additionalProperties={})]
|
||
2017-09-14 14:59:12 INFO LoggingPodStatusWatcherImpl:54 - State changed, new state:
|
||
pod name: spark-pi-1505372339796-driver
|
||
namespace: spark-cluster
|
||
labels: spark-app-selector -> spark<span class="hljs-_">-f</span>4d3a5d3ad964a05a51feb6191d50357, spark-role -> driver
|
||
pod uid: 304cf440-991a-11e7-970c<span class="hljs-_">-f</span>4e9d49f8ed0
|
||
creation time: 2017-09-14T06:59:01Z
|
||
service account name: spark
|
||
volumes: spark-token-zr8wv
|
||
node name: 172.20.0.114
|
||
start time: 2017-09-14T06:59:01Z
|
||
container images: sz-pg-oam-docker-hub-001.tendcloud.com/library/spark-driver:v2.1.0-kubernetes-0.3.1-1
|
||
phase: Succeeded
|
||
status: [ContainerStatus(containerID=docker://5c5c821c482a1e35552adccb567020532b79244392374f25754f0050e6<span class="hljs-built_in">cd</span>4c62, image=sz-pg-oam-docker-hub-001.tendcloud.com/library/spark-driver:v2.1.0-kubernetes-0.3.1-1, imageID=docker-pullable://sz-pg-oam-docker-hub-001.tendcloud.com/library/spark-driver@sha256:beb92a3e3f178e286d9e5baebdead88b5ba76d651f347ad2864bb6f8eda26f94, lastState=ContainerState(running=null, terminated=null, waiting=null, additionalProperties={}), name=spark-kubernetes-driver, ready=<span class="hljs-literal">false</span>, restartCount=0, state=ContainerState(running=null, terminated=ContainerStateTerminated(containerID=docker://5c5c821c482a1e35552adccb567020532b79244392374f25754f0050e6<span class="hljs-built_in">cd</span>4c62, <span class="hljs-built_in">exit</span>Code=0, finishedAt=2017-09-14T06:59:11Z, message=null, reason=Completed, signal=null, startedAt=null, additionalProperties={}), waiting=null, additionalProperties={}), additionalProperties={})]
|
||
2017-09-14 14:59:12 INFO LoggingPodStatusWatcherImpl:54 - Container final statuses:
|
||
|
||
|
||
Container name: spark-kubernetes-driver
|
||
Container image: sz-pg-oam-docker-hub-001.tendcloud.com/library/spark-driver:v2.1.0-kubernetes-0.3.1-1
|
||
Container state: Terminated
|
||
Exit code: 0
|
||
2017-09-14 14:59:12 INFO Client:54 - Application spark-pi finished.
|
||
</code></pre>
|
||
<p>从日志中可以看到任务运行的状态信息。</p>
|
||
<p>使用下面的命令可以看到 kubernetes 启动的 Pod 信息：</p>
|
||
<pre><code class="lang-bash">kubectl --namespace spark-cluster get pods -w
|
||
</code></pre>
|
||
<p>将会看到 <code>spark-driver</code> 和 <code>spark-exec</code> 的 Pod 信息。</p>
|
||
<h2 id="依赖管理">依赖管理</h2>
|
||
<p>上文中我们在运行测试程序时，命令行中指定的 jar 文件已包含在 docker 镜像中，是不是说我们每次提交任务都需要重新创建一个镜像呢？非也！如果真是这样也太麻烦了。</p>
|
||
<h4 id="创建-resource-staging-server">创建 resource staging server</h4>
|
||
<p>为了方便用户提交任务，不需要每次提交任务的时候都创建一个镜像，我们使用了 <strong>resource staging server</strong> 。</p>
|
||
<pre><code>kubectl create -f conf/kubernetes-resource-staging-server.yaml
|
||
</code></pre><p>我们同样将其部署在 <code>spark-cluster</code> namespace 下，该 yaml 文件见 <a href="https://github.com/rootsongjc/kubernetes-handbook" target="_blank">kubernetes-handbook</a> 的 <code>manifests/spark-with-kubernetes-native-scheduler</code> 目录。</p>
|
||
<h4 id="优化">优化</h4>
|
||
<p>其中有一点需要优化，在使用下面的命令提交任务时，使用 <code>--conf spark.kubernetes.resourceStagingServer.uri</code> 参数指定 <em>resource staging server</em> 地址，用户不应该关注 <em>resource staging server</em> 究竟运行在哪台宿主机上，可以使用下面两种方式实现：</p>
|
||
<ul>
|
||
<li>使用 <code>nodeSelector</code> 将 <em>resource staging server</em> 固定调度到某一台机器上，该地址依然使用宿主机的 IP 地址</li>
|
||
<li>改变 <code>spark-resource-staging-service</code> service 的 type 为 <strong>ClusterIP</strong>， 然后使用 <strong>Ingress</strong> 将其暴露到集群外部，然后加入的内网 DNS 里，用户使用 DNS 名称指定 <em>resource staging server</em> 的地址。</li>
|
||
</ul>
|
||
<p>然后可以执行下面的命令来提交本地的 jar 到 kubernetes 上运行。</p>
|
||
<pre><code class="lang-bash">./spark-submit \
|
||
--deploy-mode cluster \
|
||
--class org.apache.spark.examples.SparkPi \
|
||
--master k8s://https://172.20.0.113:6443 \
|
||
--kubernetes-namespace spark-cluster \
|
||
--conf spark.kubernetes.authenticate.driver.serviceAccountName=spark \
|
||
--conf spark.executor.instances=5 \
|
||
--conf spark.app.name=spark-pi \
|
||
--conf spark.kubernetes.driver.docker.image=sz-pg-oam-docker-hub-001.tendcloud.com/library/spark-driver:v2.1.0-kubernetes-0.3.1-1 \
|
||
--conf spark.kubernetes.executor.docker.image=sz-pg-oam-docker-hub-001.tendcloud.com/library/spark-executor:v2.1.0-kubernetes-0.3.1-1 \
|
||
--conf spark.kubernetes.initcontainer.docker.image=sz-pg-oam-docker-hub-001.tendcloud.com/library/spark-init:v2.1.0-kubernetes-0.3.1-1 \
|
||
--conf spark.kubernetes.resourceStagingServer.uri=http://172.20.0.114:31000 \
|
||
../examples/jars/spark-examples_2.11-2.2.0-k8s-0.4.0-SNAPSHOT.jar
|
||
</code></pre>
|
||
<p>该命令将提交本地的 <code>../examples/jars/spark-examples_2.11-2.2.0-k8s-0.4.0-SNAPSHOT.jar</code> 文件到 <em>resource staging server</em>，executor 将从该 server 上获取 jar 包并运行，这样用户就不需要每次提交任务都编译一个镜像了。</p>
|
||
<p>详见：<a href="https://apache-spark-on-k8s.github.io/userdocs/running-on-kubernetes.html#dependency-management" target="_blank">https://apache-spark-on-k8s.github.io/userdocs/running-on-kubernetes.html#dependency-management</a></p>
|
||
<h4 id="设置-hdfs-用户">设置 HDFS 用户</h4>
|
||
<p>如果 Hadoop 集群没有设置 kerbros 安全认证的话，在指定 <code>spark-submit</code> 的时候可以通过指定如下四个环境变量， 设置 Spark 与 HDFS 通信使用的用户：</p>
|
||
<pre><code class="lang-bash"> --conf spark.kubernetes.driverEnv.SPARK_USER=hadoop
|
||
--conf spark.kubernetes.driverEnv.HADOOP_USER_NAME=hadoop
|
||
--conf spark.executorEnv.HADOOP_USER_NAME=hadoop
|
||
--conf spark.executorEnv.SPARK_USER=hadoop
|
||
</code></pre>
|
||
<p>使用 hadoop 用户提交本地 jar 包的命令示例：</p>
|
||
<pre><code class="lang-bash">./spark-submit \
|
||
--deploy-mode cluster \
|
||
--class com.talkingdata.alluxio.hadooptest \
|
||
--master k8s://https://172.20.0.113:6443 \
|
||
--kubernetes-namespace spark-cluster \
|
||
--conf spark.kubernetes.driverEnv.SPARK_USER=hadoop \
|
||
--conf spark.kubernetes.driverEnv.HADOOP_USER_NAME=hadoop \
|
||
--conf spark.executorEnv.HADOOP_USER_NAME=hadoop \
|
||
--conf spark.executorEnv.SPARK_USER=hadoop \
|
||
--conf spark.kubernetes.authenticate.driver.serviceAccountName=spark \
|
||
--conf spark.executor.instances=5 \
|
||
--conf spark.app.name=spark-pi \
|
||
--conf spark.kubernetes.driver.docker.image=sz-pg-oam-docker-hub-001.tendcloud.com/library/spark-driver:v2.1.0-kubernetes-0.3.1-1 \
|
||
--conf spark.kubernetes.executor.docker.image=sz-pg-oam-docker-hub-001.tendcloud.com/library/spark-executor:v2.1.0-kubernetes-0.3.1-1 \
|
||
--conf spark.kubernetes.initcontainer.docker.image=sz-pg-oam-docker-hub-001.tendcloud.com/library/spark-init:v2.1.0-kubernetes-0.3.1-1 \
|
||
--conf spark.kubernetes.resourceStagingServer.uri=http://172.20.0.114:31000 \
|
||
~/Downloads/tendcloud_2.10-1.0.jar
|
||
</code></pre>
|
||
<p>详见：<a href="https://github.com/apache-spark-on-k8s/spark/issues/408" target="_blank">https://github.com/apache-spark-on-k8s/spark/issues/408</a></p>
|
||
<h4 id="限制-driver-和-executor-的资源使用">限制 Driver 和 Executor 的资源使用</h4>
|
||
<p>在执行 <code>spark-submit</code> 时使用如下参数设置内存和 CPU 资源限制：</p>
|
||
<pre><code class="lang-bash">--conf spark.driver.memory=3G
|
||
--conf spark.executor.memory=3G
|
||
--conf spark.driver.cores=2
|
||
--conf spark.executor.cores=10
|
||
</code></pre>
|
||
<p>这几个参数中值如何传递到 Pod 的资源设置中的呢？</p>
|
||
<p>比如我们设置在执行 <code>spark-submit</code> 的时候传递了这样的两个参数：<code>--conf spark.driver.cores=2</code> 和 <code>--conf spark.driver.memory=100G</code> 那么查看 driver pod 的 yaml 输出结果将会看到这样的资源设置：</p>
|
||
<pre><code class="lang-yaml"><span class="hljs-attr"> resources:</span>
|
||
<span class="hljs-attr"> limits:</span>
|
||
<span class="hljs-attr"> memory:</span> <span class="hljs-number">110</span>Gi
|
||
<span class="hljs-attr"> requests:</span>
|
||
<span class="hljs-attr"> cpu:</span> <span class="hljs-string">"2"</span>
|
||
<span class="hljs-attr"> memory:</span> <span class="hljs-number">100</span>Gi
|
||
</code></pre>
|
||
<p>以上参数是对 <code>request</code> 值的设置，那么 <code>limit</code> 的资源设置的值又是从何而来？</p>
|
||
<p>可以使用 <code>spark.kubernetes.driver.limit.cores</code> 和 <code>spark.kubernetes.executor.limit.cores</code> 来设置 CPU的 hard limit。</p>
|
||
<p>memory limit 的值是根据 memory request 的值加上 <code>spark.kubernetes.executor.memoryOverhead</code> 的值计算而来的，该配置项用于设置分配给每个 executor 的超过 heap 内存的值（可以使用k、m、g单位）。该值用于虚拟机的开销、其他本地服务开销。根据 executor 的大小设置（通常是 6%到10%）。</p>
|
||
<p>我们可以这样来提交一个任务，同时设置 driver 和 executor 的 CPU、内存的资源 request 和 limit 值（driver 的内存 limit 值为 request 值的 110%）。</p>
|
||
<pre><code class="lang-bash">./spark-submit \
|
||
--deploy-mode cluster \
|
||
--class org.apache.spark.examples.SparkPi \
|
||
--master k8s://https://172.20.0.113:6443 \
|
||
--kubernetes-namespace spark-cluster \
|
||
--conf spark.kubernetes.authenticate.driver.serviceAccountName=spark \
|
||
--conf spark.driver.memory=100G \
|
||
--conf spark.executor.memory=10G \
|
||
--conf spark.driver.cores=30 \
|
||
--conf spark.executor.cores=2 \
|
||
--conf spark.driver.maxResultSize=10240m \
|
||
--conf spark.kubernetes.driver.limit.cores=32 \
|
||
--conf spark.kubernetes.executor.limit.cores=3 \
|
||
--conf spark.kubernetes.executor.memoryOverhead=2g \
|
||
--conf spark.executor.instances=5 \
|
||
--conf spark.app.name=spark-pi \
|
||
--conf spark.kubernetes.driver.docker.image=sz-pg-oam-docker-hub-001.tendcloud.com/library/spark-driver:v2.1.0-kubernetes-0.3.1-1 \
|
||
--conf spark.kubernetes.executor.docker.image=sz-pg-oam-docker-hub-001.tendcloud.com/library/spark-executor:v2.1.0-kubernetes-0.3.1-1 \
|
||
--conf spark.kubernetes.initcontainer.docker.image=sz-pg-oam-docker-hub-001.tendcloud.com/library/spark-init:v2.1.0-kubernetes-0.3.1-1 \
|
||
<span class="hljs-built_in">local</span>:///opt/spark/examples/jars/spark-examples_2.11-2.2.0-k8s-0.4.0-SNAPSHOT.jar 10000000
|
||
</code></pre>
|
||
<p>这将启动一个包含一千万个 task 的计算 pi 的 spark 任务，任务运行过程中，drvier 的 CPU 实际消耗大约为 3 核，内存 40G，每个 executor 的 CPU 实际消耗大约不到 1 核，内存不到 4G，我们可以根据实际资源消耗不断优化资源的 request 值。</p>
|
||
<p><code>SPARK_DRIVER_MEMORY</code> 和 <code>SPARK_EXECUTOR_MEMORY</code> 和分别作为 Driver 容器和 Executor 容器启动的环境变量，比如下面这个 Driver 启动的 CMD 中：</p>
|
||
<pre><code class="lang-bash">CMD SPARK_CLASSPATH=<span class="hljs-string">"<span class="hljs-variable">${SPARK_HOME}</span>/jars/*"</span> && \
|
||
env | grep SPARK_JAVA_OPT_ | sed <span class="hljs-string">'s/[^=]*=\(.*\)/\1/g'</span> > /tmp/java_opts.txt && \
|
||
<span class="hljs-built_in">readarray</span> -t SPARK_DRIVER_JAVA_OPTS < /tmp/java_opts.txt && \
|
||
<span class="hljs-keyword">if</span> ! [ -z <span class="hljs-variable">${SPARK_MOUNTED_CLASSPATH+x}</span> ]; <span class="hljs-keyword">then</span> SPARK_CLASSPATH=<span class="hljs-string">"<span class="hljs-variable">$SPARK_MOUNTED_CLASSPATH</span>:<span class="hljs-variable">$SPARK_CLASSPATH</span>"</span>; <span class="hljs-keyword">fi</span> && \
|
||
<span class="hljs-keyword">if</span> ! [ -z <span class="hljs-variable">${SPARK_SUBMIT_EXTRA_CLASSPATH+x}</span> ]; <span class="hljs-keyword">then</span> SPARK_CLASSPATH=<span class="hljs-string">"<span class="hljs-variable">$SPARK_SUBMIT_EXTRA_CLASSPATH</span>:<span class="hljs-variable">$SPARK_CLASSPATH</span>"</span>; <span class="hljs-keyword">fi</span> && \
|
||
<span class="hljs-keyword">if</span> ! [ -z <span class="hljs-variable">${SPARK_EXTRA_CLASSPATH+x}</span> ]; <span class="hljs-keyword">then</span> SPARK_CLASSPATH=<span class="hljs-string">"<span class="hljs-variable">$SPARK_EXTRA_CLASSPATH</span>:<span class="hljs-variable">$SPARK_CLASSPATH</span>"</span>; <span class="hljs-keyword">fi</span> && \
|
||
<span class="hljs-keyword">if</span> ! [ -z <span class="hljs-variable">${SPARK_MOUNTED_FILES_DIR+x}</span> ]; <span class="hljs-keyword">then</span> cp -R <span class="hljs-string">"<span class="hljs-variable">$SPARK_MOUNTED_FILES_DIR</span>/."</span> .; <span class="hljs-keyword">fi</span> && \
|
||
<span class="hljs-keyword">if</span> ! [ -z <span class="hljs-variable">${SPARK_MOUNTED_FILES_FROM_SECRET_DIR}</span> ]; <span class="hljs-keyword">then</span> cp -R <span class="hljs-string">"<span class="hljs-variable">$SPARK_MOUNTED_FILES_FROM_SECRET_DIR</span>/."</span> .; <span class="hljs-keyword">fi</span> && \
|
||
<span class="hljs-variable">${JAVA_HOME}</span>/bin/java <span class="hljs-string">"<span class="hljs-variable">${SPARK_DRIVER_JAVA_OPTS[@]}</span>"</span> -cp <span class="hljs-variable">$SPARK_CLASSPATH</span> -Xms<span class="hljs-variable">$SPARK_DRIVER_MEMORY</span> -Xmx<span class="hljs-variable">$SPARK_DRIVER_MEMORY</span> <span class="hljs-variable">$SPARK_DRIVER_CLASS</span> <span class="hljs-variable">$SPARK_DRIVER_ARGS</span>
|
||
</code></pre>
|
||
<p>我们可以看到对 <code>SPARK_DRIVER_MEMORY</code> 环境变量的引用。Executor 的设置与 driver 类似。</p>
|
||
<p>而我们可以使用这样的参数来传递环境变量的值 <code>spark.executorEnv.[EnvironmentVariableName]</code>，只要将 <code>EnvironmentVariableName</code> 替换为环境变量名称即可。</p>
|
||
<h2 id="参考">参考</h2>
|
||
<p><a href="http://lxw1234.com/archives/2015/12/593.htm" target="_blank">Spark动态资源分配-Dynamic Resource Allocation</a></p>
|
||
<p><a href="https://apache-spark-on-k8s.github.io/userdocs/running-on-kubernetes.html" target="_blank">Running Spark on Kubernetes</a></p>
|
||
<p><a href="https://issues.apache.org/jira/browse/SPARK-18278" target="_blank">Apache Spark Jira Issue - 18278 - SPIP: Support native submission of spark jobs to a kubernetes cluster</a></p>
|
||
<p><a href="https://github.com/kubernetes/kubernetes/issues/34377" target="_blank">Kubernetes Github Issue - 34377 Support Spark natively in Kubernetes</a></p>
|
||
<p><a href="https://github.com/kubernetes/kubernetes/tree/master/examples/spark" target="_blank">Kubernetes example spark</a></p>
|
||
<p><a href="https://github.com/rootsongjc/spark-on-kubernetes" target="_blank">https://github.com/rootsongjc/spark-on-kubernetes</a></p>
|
||
<p><a href="https://github.com/apache-spark-on-k8s/spark/blob/branch-2.2-kubernetes/resource-managers/kubernetes/architecture-docs/scheduler-backend.md" target="_blank">Scheduler backend</a></p>
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