如何把Spring Cloud Data Flow部署在Kubernetes上

1 前言

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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