k8s部署xxl-job

xxl-job是个很好用的定时任务调度器,记录下部署过程

第一步看官方文档

https://www.xuxueli.com/xxl-job

先去这里把xxljob相关的表导入

https://github.com/xuxueli/xxl-job/tree/master/doc/db

部署xxl-job-admin,创建xxl-job-admin.yaml文件

xxl-job-admin的部署

apiVersion: v1
kind: ConfigMap
apiVersion: v1
metadata:
  name: xxl-job-config
  namespace: insight
data:
  PARAMS: >-
    --server.port=9333
    --spring.datasource.url=jdbc:mysql://jddxxxxxxxxxxx.com/insight?useUnicode=true&characterEncoding=UTF-8&autoReconnect=true&serverTimezone=Asia/Shanghai
    --spring.datasource.username=txxxxxo
    --spring.datasource.password=Txxxxx4
    --xxl.job.accessToken=22d697201b8953eabf5f8d901f30a037
---
kind: Deployment
apiVersion: apps/v1
metadata:
  name: xxl-job-admin
  namespace: insight
  labels:
    app: xxl-job-admin
spec:
  replicas: 1
  selector:
    matchLabels:
      app: xxl-job-admin
  template:
    metadata:
      creationTimestamp: null
      labels:
        app: xxl-job-admin
    spec:
      containers:
        - name: xxl-job-admin
          image: xuxueli/xxl-job-admin:3.0.0
          ports:
            - containerPort: 9333
              protocol: TCP
          env:
            - name: TZ
              value: Asia/Shanghai
            - name: PARAMS
              valueFrom:
                configMapKeyRef:
                  name: xxl-job-config
                  key: PARAMS
          resources:
            limits:
              cpu: '1'
              memory: 2Gi
            requests:
              cpu: 500m
              memory: 512Mi
          terminationMessagePath: /dev/termination-log
          terminationMessagePolicy: File
          imagePullPolicy: Always
---
apiVersion: v1
kind: Service
metadata:
  name: xxl-job-admin-svc
  namespace: insight
  labels:
    app: xxl-job-admin-svc
spec:
  ports:
    - name: xxl-job-admin
      port: 9333
      protocol: TCP
      targetPort: 9333
  selector:
    app: xxl-job-admin
  type: ClusterIP



---
# ------------------- App Ingress ------------------- #
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: xxl-job-admin
  namespace: insight
spec:
  ingressClassName: nginx
  rules:
  - host: xxl-job-admin.com
    http:
      paths:
      - path: /
        pathType: Prefix
        backend:
          service:
            name: xxl-job-admin-svc
            port:
              name: xxl-job-admin

1. 这里我修改了端口号原来是8080 现在是9333

2. 添加k8s命名空间insight,可以不要就是默认default.

3. 因为我的集群是通过Ingress暴露的需要外部访问xxl-job-admin,所以我加了Ingress,这个也可以删掉

执行 kubectl apply -f xxl-job-admin.yaml就可以了

访问管理后台
http://xxl-job-admin.com:30937/xxl-job-admin/j (这个地址是我的ingress暴露地址.)
admin/123456

执行器的部署

执行器参考官方样例

https://github.com/xuxueli/xxl-job/tree/master/xxl-job-executor-samples

我的执行器也是springboot应用

server:
  port: 9444    #springboot的端口

spring:
  profiles:
    active: prod
  config:
    import:
      - optional:classpath:application.yaml
      - optional:classpath:application-${spring.profiles.active}.yaml
      - optional:nacos:${spring.application.name}-${spring.profiles.active}.yaml

xxl:
  job:
    admin:
      addresses: http://xxl-job-admin-svc:9333/xxl-job-admin  # 调度中心部署地址
    accessToken: 22d697201b8953eabf5f8d901f30a037             # 执行器通信TOKEN，非空时启用
    executor:
      appname: xxl-job-executor-sample               # 执行器AppName
      address:
      ip:                                        # 执行器IP(选填)：默认为空表示自动获取IP
      port: 9999                                     # 执行器端口号
      logpath: ./logs/xxl-job/jobhandler
      logretentiondays: 30


mybatis-plus:
  mapper-locations: classpath*:/mapper/**/*.xml
  type-aliases-package: com.xxxxx.xxljob.model
  configuration:
    log-impl: org.apache.ibatis.logging.stdout.StdOutImpl

主要是看xxl.job的配置.

这里9444与9999端口是独立的,一个是这个应用的端口,另一个是xxl-job执行器端口

这是执行器部署文件

apiVersion: v1
kind: Service
metadata:
  name: insight-xxljob
  namespace: insight
spec:
  type: ClusterIP
  ports:
  - name: xxl-job-web
    port: 9444
    targetPort: 9444
  - name: xxl-job-executor
    port: 9999
    targetPort: 9999
  selector:
    app: insight-xxljob
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: insight-xxljob
  namespace: insight
spec:
  selector:
    matchLabels:
      app: insight-xxljob
  replicas: 1
  strategy:
    rollingUpdate:
      maxSurge: 50%
      maxUnavailable: 50%
    type: RollingUpdate
  template:
    metadata:
      labels:
        app: insight-xxljob
    spec:
      imagePullSecrets:
      - name: yiyitech-harbor 
      containers:
      - name: insight-xxljob
        image: harbor.xxxxx.com:81/insight/yiyitech-xxljob:master
        imagePullPolicy: Always
        envFrom:
        - configMapRef:
            name: insight-nacos-config
        env:
        - name: JAVA_OPTS
          value: -server -Xms3g -Xmx3g -Xmn300m -XX:+UseG1GC -XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/mnt/logs/joyi/pop-master-gc.hprof
        resources:
          limits:
            cpu: 1
            memory: 2G
          requests:
            cpu: 0.3
            memory: 2G
        livenessProbe:
          httpGet:
            path: /actuator/health/liveness
            port: 9444
            scheme: HTTP
          initialDelaySeconds: 40
          timeoutSeconds: 3
          periodSeconds: 10
          successThreshold: 1
          failureThreshold: 3
        readinessProbe:
          httpGet:
            path: /actuator/health/readiness
            port: 9444
            scheme: HTTP
          initialDelaySeconds: 40
          timeoutSeconds: 3
          periodSeconds: 10
          successThreshold: 1
          failureThreshold: 3
        ports:
        - containerPort: 9444
        volumeMounts:
        - mountPath: /mnt/logs/joyi
          name: log-dir
      nodeSelector:
        insight-master: insight-master
      volumes:
      - name: log-dir
        hostPath:
          path: /root/data/logs/k8s/joyi
          type: Directory

需要注意的点只有Service要暴露两个端口9999与9444.

上面很多东西比如命名空间,imagePullSecrets,镜像拉取地址,nodeSelector根据自己项目情况增删.

可能会用到的命令

查看pod的状态
kubectl -n insight get pods    
查看详细信息(查找启动失败原因)
kubectl -n insight describe pod xxxx      
查看容器日志
kubectl -n insight logs -f --tail 100   xxxx

删除pod
kubectl delete -f xxx
生成pod
kubectl apply -f  xxx

​