如何把Spring Cloud Data Flow部署在Kubernetes上

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 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/kubernetsjCgfAdkes/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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