1 前言

Spring Cloud Data Flow在本地跑得好好的，為什么要部署在Kubernetes上呢？主要是因?yàn)镵ubernetes能提供更靈活的微服務(wù)管理；在集群上跑，會(huì)更安全穩(wěn)定、更合理利用物理資源。

Spring Cloud Data Flow入門簡(jiǎn)介請(qǐng)參考：Spring Cloud Data Flow初體驗(yàn)，以Local模式運(yùn)行

2 部署Data Flow到Kubernetes

以簡(jiǎn)單為原則，我們依然是基于Batch任務(wù)，不部署與Stream相關(guān)的組件。

2.1 下載GitHub代碼

我們要基于官方提供的部署代碼進(jìn)行修改，先把官方代碼clone下來(lái)：

$ git clone https://github.com/spring-cloud/spring-cloud-dataflow.git

我們切換到最新穩(wěn)定版本的代碼版本：

$ git checkout v2.5.3.RELEASE

2.2 創(chuàng)建權(quán)限賬號(hào)

為了讓Data Flow Server有權(quán)限來(lái)跑任務(wù)，能在Kubernetes管理資源，如新建Pod等，所以要?jiǎng)?chuàng)建對(duì)應(yīng)的權(quán)限賬號(hào)。這部分代碼與源碼一致，不需要修改：

（1）server-roles.yaml

kind: RoleapiVersion: rbac.authorization.k8s.io/v1metadata: name: scdf-rolerules: - apiGroups: [''] resources: ['services', 'pods', 'replicationcontrollers', 'persistentvolumeclaims'] verbs: ['get', 'list', 'watch', 'create', 'delete', 'update'] - apiGroups: [''] resources: ['configmaps', 'secrets', 'pods/log'] verbs: ['get', 'list', 'watch'] - apiGroups: ['apps'] resources: ['statefulsets', 'deployments', 'replicasets'] verbs: ['get', 'list', 'watch', 'create', 'delete', 'update', 'patch'] - apiGroups: ['extensions'] resources: ['deployments', 'replicasets'] verbs: ['get', 'list', 'watch', 'create', 'delete', 'update', 'patch'] - apiGroups: ['batch'] resources: ['cronjobs', 'jobs'] verbs: ['create', 'delete', 'get', 'list', 'watch', 'update', 'patch']

（2）server-rolebinding.yaml

kind: RoleBindingapiVersion: rbac.authorization.k8s.io/v1beta1metadata: name: scdf-rbsubjects:- kind: ServiceAccount name: scdf-saroleRef: kind: Role name: scdf-role apiGroup: rbac.authorization.k8s.io

（3）service-account.yaml

apiVersion: v1kind: ServiceAccountmetadata: name: scdf-sa

執(zhí)行以下命令，創(chuàng)建對(duì)應(yīng)賬號(hào)：

$ kubectl create -f src/kubernetes/server/server-roles.yaml $ kubectl create -f src/kubernetes/server/server-rolebinding.yaml $ kubectl create -f src/kubernetes/server/service-account.yaml

執(zhí)行完成后，可以檢查一下：

$ kubectl get roleNAME AGEscdf-role 119m$ kubectl get rolebindingNAME AGEscdf-rb 117m$ kubectl get serviceAccountNAME SECRETS AGEdefault 1 27dscdf-sa 1 117m

2.3 部署MySQL

可以選擇其它數(shù)據(jù)庫(kù)，如果本來(lái)就有數(shù)據(jù)庫(kù)，可以不用部署，在部署Server的時(shí)候改一下配置就好了。這里跟著官方的Guide來(lái)。為了保證部署不會(huì)因?yàn)殓R像下載問(wèn)題而失敗，我提前下載了鏡像：

$ docker pull mysql:5.7.25

MySQL的yaml文件也不需要修改，直接執(zhí)行以下命令即可：

$ kubectl create -f src/kubernetes/mysql/

執(zhí)行完后檢查一下：

$ kubectl get SecretNAME TYPE DATA AGEdefault-token-jhgfp kubernetes.io/service-account-token 3 27dmysql Opaque 2 98mscdf-sa-token-wmgk6 kubernetes.io/service-account-token 3 123m$ kubectl get PersistentVolumeClaimNAME STATUS VOLUME CAPACITY ACCESS MODES STORAGECLASS AGEmysql Bound pvc-e95b495a-bea5-40ee-9606-dab8d9b0d65c 8Gi RWO hostpath 98m$ kubectl get DeploymentNAME READY UP-TO-DATE AVAILABLE AGEmysql 1/1 1 1 98m$ kubectl get ServiceNAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGEmysql ClusterIP 10.98.243.130 <none> 3306/TCP 98m

2.4 部署Data Flow Server

2.4.1 修改配置文件server-config.yaml

刪除掉不用的配置，主要是Prometheus和Grafana的配置，結(jié)果如下：

apiVersion: v1kind: ConfigMapmetadata: name: scdf-server labels: app: scdf-serverdata: application.yaml: |- spring: cloud: dataflow: task: platform: kubernetes: accounts: default: limits: memory: 1024Mi datasource: url: jdbc:mysql://${MYSQL_SERVICE_HOST}:${MYSQL_SERVICE_PORT}/mysql username: root password: ${mysql-root-password} driverClassName: org.mariadb.jdbc.Driver testOnBorrow: true validationQuery: 'SELECT 1'

2.4.2 修改server-svc.yaml

因?yàn)槲沂潜镜剡\(yùn)行的Kubernetes，所以把Service類型從LoadBalancer改為NodePort，并配置端口為30093。

kind: ServiceapiVersion: v1metadata: name: scdf-server labels: app: scdf-server spring-deployment-id: scdfspec: # If you are running k8s on a local dev box or using minikube, you can use type NodePort instead type: NodePort ports: - port: 80 name: scdf-server nodePort: 30093 selector: app: scdf-server

2.4.3 修改server-deployment.yaml

主要把Stream相關(guān)的去掉，如SPRING_CLOUD_SKIPPER_CLIENT_SERVER_URI配置項(xiàng)：

apiVersion: apps/v1kind: Deploymentmetadata: name: scdf-server labels: app: scdf-serverspec: selector: matchLabels: app: scdf-server replicas: 1 template: metadata: labels: app: scdf-server spec: containers: - name: scdf-server image: springcloud/spring-cloud-dataflow-server:2.5.3.RELEASE imagePullPolicy: IfNotPresent volumeMounts: - name: database mountPath: /etc/secrets/database readOnly: true ports: - containerPort: 80 livenessProbe: httpGet: path: /management/health port: 80 initialDelaySeconds: 45 readinessProbe: httpGet: path: /management/info port: 80 initialDelaySeconds: 45 resources: limits: cpu: 1.0 memory: 2048Mi requests: cpu: 0.5 memory: 1024Mi env: - name: KUBERNETES_NAMESPACE valueFrom: fieldRef: fieldPath: 'metadata.namespace' - name: SERVER_PORT value: ’80’ - name: SPRING_CLOUD_CONFIG_ENABLED value: ’false’ - name: SPRING_CLOUD_DATAFLOW_FEATURES_ANALYTICS_ENABLED value: ’true’ - name: SPRING_CLOUD_DATAFLOW_FEATURES_SCHEDULES_ENABLED value: ’true’ - name: SPRING_CLOUD_KUBERNETES_SECRETS_ENABLE_API value: ’true’ - name: SPRING_CLOUD_KUBERNETES_SECRETS_PATHS value: /etc/secrets - name: SPRING_CLOUD_KUBERNETES_CONFIG_NAME value: scdf-server - name: SPRING_CLOUD_DATAFLOW_SERVER_URI value: ’http://${SCDF_SERVER_SERVICE_HOST}:${SCDF_SERVER_SERVICE_PORT}’ # Add Maven repo for metadata artifact resolution for all stream apps - name: SPRING_APPLICATION_JSON value: '{ 'maven': { 'local-repository': null, 'remote-repositories': { 'repo1': { 'url': 'https://repo.spring.io/libs-snapshot'} } } }' initContainers: - name: init-mysql-wait image: busybox command: [’sh’, ’-c’, ’until nc -w3 -z mysql 3306; do echo waiting for mysql; sleep 3; done;’] serviceAccountName: scdf-sa volumes: - name: database secret: secretName: mysql

2.4.4 部署Server

完成文件修改后，就可以執(zhí)行以下命令部署了：

# 提前下載鏡像$ docker pull springcloud/spring-cloud-dataflow-server:2.5.3.RELEASE# 部署Data Flow Server$ kubectl create -f src/kubernetes/server/server-config.yaml $ kubectl create -f src/kubernetes/server/server-svc.yaml $ kubectl create -f src/kubernetes/server/server-deployment.yaml

執(zhí)行完成，沒(méi)有錯(cuò)誤就可以訪問(wèn)：http://localhost:30093/dashboard/

3 運(yùn)行一個(gè)Task

檢驗(yàn)是否部署成功最簡(jiǎn)單的方式就是跑一個(gè)任務(wù)試試。還是按以前的步驟，先注冊(cè)應(yīng)用，再定義Task，然后執(zhí)行。

我們依舊使用官方已經(jīng)準(zhǔn)備好的應(yīng)用，但要注意這次我們選擇是的Docker格式，而不是jar包了。

成功執(zhí)行后，查看Kubernetes的Dashboard，能看到一個(gè)剛創(chuàng)建的Pod：

4 總結(jié)

本文通過(guò)一步步講解，把Spring Cloud Data Flow成功部署在了Kubernetes上，并成功在Kubenetes上跑了一個(gè)任務(wù)，再也不再是Local本地單機(jī)模式了。

到此這篇關(guān)于把Spring Cloud Data Flow部署在Kubernetes上，再跑個(gè)任務(wù)試試的文章就介紹到這了,更多相關(guān)把Spring Cloud Data Flow部署在Kubernetes上，再跑個(gè)任務(wù)試試內(nèi)容請(qǐng)搜索好吧啦網(wǎng)以前的文章或繼續(xù)瀏覽下面的相關(guān)文章希望大家以后多多支持好吧啦網(wǎng)！