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：
|
1
|
$ 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：
|
1
|
$ 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
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
|
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
|
1
2
3
4
5
6
7
8
9
10
11
|
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
|
1
2
3
4
|
apiVersion: v1kind: ServiceAccountmetadata: name: scdf-sa |
执行以下命令，创建对应账号：
|
1
2
3
|
$ 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：
|
1
2
3
4
5
6
7
8
9
10
11
12
|
$ 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：
|
1
|
$ 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：
|
1
|
$ kubectl create -f src/kubernetes/mysql/ |
执行完后检查一下：
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
|
$ 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
删除掉不用的配置，主要是Prometheus和Grafana的配置，结果如下：
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
|
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。
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
|
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相关的去掉，如SPRING_CLOUD_SKIPPER_CLIENT_SERVER_URI配置项：
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
|
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}:${SCDF_SERVER_SERVICE_PORT}' # Add Maven repo for metadata artifact resolution for all stream apps - name: SPRING_APPLICATION_JSON value: "{ \"maven\": { \"local-repository\": null, \"remote-repositories\": { \"repo1\": { \"url\": \"https://repo.spring.io/libs-snapshot\"} } } }" initContainers: - name: init-mysql-wait image: busybox command: ['sh', '-c', 'until nc -w3 -z mysql 3306; do echo waiting for mysql; sleep 3; done;'] serviceAccountName: scdf-sa volumes: - name: database secret: secretName: mysql |
2.4.4 部署Server
完成文件修改后，就可以执行以下命令部署了：
|
1
2
3
4
5
6
7
|
# 提前下载镜像$ 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。
到此这篇关于把Spring Cloud Data Flow部署在Kubernetes上，再跑个任务试试的文章就介绍到这了,更多相关把Spring Cloud Data Flow部署在Kubernetes上，再跑个任务试试内容请搜索服务器之家以前的文章或继续浏览下面的相关文章希望大家以后多多支持服务器之家！
原文链接：https://www.cnblogs.com/larrydpk/archive/2020/08/03/13424280.html
