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？主要是因為Kubernetes能提供更靈活的微服務管理；在集群上跑，會更安全穩定、更合理利用物理資源。
Spring Cloud Data Flow入門簡介請參考：Spring Cloud Data Flow初體驗，以Local模式運行
2 部署Data Flow到Kubernetes
以簡單為原則，我們依然是基于Batch任務，不部署與Stream相關的組件。
2.1 下載GitHub代碼
我們要基于官方提供的部署代碼進行修改，先把官方代碼clone下來：
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$ git clone https://github.com/spring-cloud/spring-cloud-dataflow.git |
我們切換到最新穩定版本的代碼版本：
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$ git checkout v2.5.3.RELEASE |
2.2 創建權限賬號
為了讓Data Flow Server有權限來跑任務，能在Kubernetes管理資源，如新建Pod等，所以要創建對應的權限賬號。這部分代碼與源碼一致，不需要修改：
（1）server-roles.yaml
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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
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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
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apiVersion: v1kind: ServiceAccountmetadata: name: scdf-sa |
執行以下命令，創建對應賬號：
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$ 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 |
執行完成后，可以檢查一下：
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$ 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
可以選擇其它數據庫，如果本來就有數據庫，可以不用部署，在部署Server的時候改一下配置就好了。這里跟著官方的Guide來。為了保證部署不會因為鏡像下載問題而失敗，我提前下載了鏡像：
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$ docker pull mysql:5.7.25 |
MySQL的yaml文件也不需要修改，直接執行以下命令即可：
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$ kubectl create -f src/kubernetes/mysql/ |
執行完后檢查一下：
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$ 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，結果如下：
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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: ${mysql-root-password} driverClassName: org.mariadb.jdbc.Driver testOnBorrow: true validationQuery: "SELECT 1" |
2.4.2 修改server-svc.yaml
因為我是本地運行的Kubernetes，所以把Service類型從LoadBalancer改為NodePort，并配置端口為30093。
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kind: ServiceapiVersion: v1metadata: name: scdf-server labels: app: scdf-server spring-deployment-id: scdfspec: # If you are running k8s on a local dev box or using minikube, you can use type NodePort instead type: NodePort ports: - port: 80 name: scdf-server nodePort: 30093 selector: app: scdf-server |
2.4.3 修改server-deployment.yaml
主要把Stream相關的去掉，如SPRING_CLOUD_SKIPPER_CLIENT_SERVER_URI配置項：
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apiVersion: apps/v1kind: Deploymentmetadata: name: scdf-server labels: app: scdf-serverspec: selector: matchLabels: app: scdf-server replicas: 1 template: metadata: labels: app: scdf-server spec: containers: - name: scdf-server image: springcloud/spring-cloud-dataflow-server:2.5.3.RELEASE imagePullPolicy: IfNotPresent volumeMounts: - name: database mountPath: /etc/secrets/database readOnly: true ports: - containerPort: 80 livenessProbe: httpGet: path: /management/health port: 80 initialDelaySeconds: 45 readinessProbe: httpGet: path: /management/info port: 80 initialDelaySeconds: 45 resources: limits: cpu: 1.0 memory: 2048Mi requests: cpu: 0.5 memory: 1024Mi env: - name: KUBERNETES_NAMESPACE valueFrom: fieldRef: fieldPath: "metadata.namespace" - name: SERVER_PORT value: '80' - name: SPRING_CLOUD_CONFIG_ENABLED value: 'false' - name: SPRING_CLOUD_DATAFLOW_FEATURES_ANALYTICS_ENABLED value: 'true' - name: SPRING_CLOUD_DATAFLOW_FEATURES_SCHEDULES_ENABLED value: 'true' - name: SPRING_CLOUD_KUBERNETES_SECRETS_ENABLE_API value: 'true' - name: SPRING_CLOUD_KUBERNETES_SECRETS_PATHS value: /etc/secrets - name: SPRING_CLOUD_KUBERNETES_CONFIG_NAME value: scdf-server - name: SPRING_CLOUD_DATAFLOW_SERVER_URI value: 'http://${SCDF_SERVER_SERVICE_HOST}:${SCDF_SERVER_SERVICE_PORT}' # Add Maven repo for metadata artifact resolution for all stream apps - name: SPRING_APPLICATION_JSON value: "{ \"maven\": { \"local-repository\": null, \"remote-repositories\": { \"repo1\": { \"url\": \"https://repo.spring.io/libs-snapshot\"} } } }" initContainers: - name: init-mysql-wait image: busybox command: ['sh', '-c', 'until nc -w3 -z mysql 3306; do echo waiting for mysql; sleep 3; done;'] serviceAccountName: scdf-sa volumes: - name: database secret: secretName: mysql |
2.4.4 部署Server
完成文件修改后，就可以執行以下命令部署了：
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# 提前下載鏡像$ 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 |
執行完成，沒有錯誤就可以訪問：http://localhost:30093/dashboard/
3 運行一個Task
檢驗是否部署成功最簡單的方式就是跑一個任務試試。還是按以前的步驟，先注冊應用，再定義Task，然后執行。
我們依舊使用官方已經準備好的應用，但要注意這次我們選擇是的Docker格式，而不是jar包了。
成功執行后，查看Kubernetes的Dashboard，能看到一個剛創建的Pod：
4 總結
本文通過一步步講解，把Spring Cloud Data Flow成功部署在了Kubernetes上，并成功在Kubenetes上跑了一個任務，再也不再是Local本地單機模式了。
到此這篇關于把Spring Cloud Data Flow部署在Kubernetes上，再跑個任務試試的文章就介紹到這了,更多相關把Spring Cloud Data Flow部署在Kubernetes上，再跑個任務試試內容請搜索服務器之家以前的文章或繼續瀏覽下面的相關文章希望大家以后多多支持服務器之家！
原文鏈接：https://www.cnblogs.com/larrydpk/archive/2020/08/03/13424280.html
