把Spring Cloud Data Flow部署在Kubernetes上，再跑个任务试试

1 前言

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Spring Cloud Data Flow在本地跑得好好的，为什么要部署在Kubernetes上呢？主要是因为Kubernetes能提供更灵活的微服务管理；在集群上跑，会更安全稳定、更合理利用物理资源。

Spring Cloud Data Flow入门简介请参考：Spring Cloud Data Flow初体验，以Local模式运行

2 部署Data Flow到Kubernetes

以简单为原则，我们依然是基于Batch任务，不部署与Stream相关的组件。

2.1 下载GitHub代码

我们要基于官方提供的部署代码进行修改，先把官方代码clone下来：

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

我们切换到最新稳定版本的代码版本：

$ git checkout v2.5.3.RELEASE

2.2 创建权限账号

为了让Data Flow Server有权限来跑任务，能在Kubernetes管理资源，如新建Pod等，所以要创建对应的权限账号。这部分代码与源码一致，不需要修改：

（1）server-roles.yaml

kind: Role
apiVersion: rbac.authorization.k8s.io/v1
metadata:
  name: scdf-role
rules:
  - 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: RoleBinding
apiVersion: rbac.authorization.k8s.io/v1beta1
metadata:
  name: scdf-rb
subjects:
- kind: ServiceAccount
  name: scdf-sa
roleRef:
  kind: Role
  name: scdf-role
  apiGroup: rbac.authorization.k8s.io

（3）service-account.yaml

apiVersion: v1
kind: ServiceAccount
metadata:
  name: scdf-sa

执行以下命令，创建对应账号：

$ 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

执行完成后，可以检查一下：

$ kubectl get role
NAME        AGE
scdf-role   119m

$ kubectl get rolebinding
NAME      AGE
scdf-rb   117m

$ kubectl get serviceAccount
NAME      SECRETS   AGE
default   1         27d
scdf-sa   1         117m

2.3 部署MySQL

可以选择其它数据库，如果本来就有数据库，可以不用部署，在部署Server的时候改一下配置就好了。这里跟着官方的Guide来。为了保证部署不会因为镜像下载问题而失败，我提前下载了镜像：

$ docker pull mysql:5.7.25

MySQL的yaml文件也不需要修改，直接执行以下命令即可：

$ kubectl create -f src/kubernetes/mysql/

执行完后检查一下：

$ kubectl get Secret
NAME                  TYPE                                  DATA   AGE
default-token-jhgfp   kubernetes.io/service-account-token   3      27d
mysql                 Opaque                                2      98m
scdf-sa-token-wmgk6   kubernetes.io/service-account-token   3      123m

$ kubectl get PersistentVolumeClaim
NAME    STATUS   VOLUME                                     CAPACITY   ACCESS MODES   STORAGECLASS   AGE
mysql   Bound    pvc-e95b495a-bea5-40ee-9606-dab8d9b0d65c   8Gi        RWO            hostpath       98m

$ kubectl get Deployment
NAME          READY   UP-TO-DATE   AVAILABLE   AGE
mysql         1/1     1            1           98m

$ kubectl get Service
NAME          TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)        AGE
mysql         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

删除掉不用的配置，主要是Prometheus和Grafana的配置，结果如下：

apiVersion: v1
kind: ConfigMap
metadata:
  name: scdf-server
  labels:
    app: scdf-server
data:
  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

因为我是本地运行的Kubernetes，所以把Service类型从LoadBalancer改为NodePort，并配置端口为30093。

kind: Service
apiVersion: v1
metadata:
  name: scdf-server
  labels:
    app: scdf-server
    spring-deployment-id: scdf
spec:
  # 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相关的去掉，如SPRING_CLOUD_SKIPPER_CLIENT_SERVER_URI配置项：

apiVersion: apps/v1
kind: Deployment
metadata:
  name: scdf-server
  labels:
    app: scdf-server
spec:
  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，就可以执行以下命令部署了：

# 提前下载镜像
$ 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

执行完成，没有错误就可以访问：http://localhost:30093/dashboard/

3 运行一个Task

检验是否部署成功最简单的方式就是跑一个任务试试。还是按以前的步骤，先注册应用，再定义Task，然后执行。

我们依旧使用官方已经准备好的应用，但要注意这次我们选择是的Docker格式，而不是jar包了。

成功执行后，查看Kubernetes的Dashboard，能看到一个刚创建的Pod：

4 总结

本文通过一步步讲解，把Spring Cloud Data Flow成功部署在了Kubernetes上，并成功在Kubenetes上跑了一个任务，再也不再是Local本地单机模式了。

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