脚本宝典收集整理的这篇文章主要介绍了Spark部署到K8S集群--standalone,脚本宝典觉得挺不错的,现在分享给大家,也给大家做个参考。
使用上个博客 (Spark部署到K8S集群--Kubernetes Native)[https://www.cnblogs.COM/regis-code/p/15470867.htML] 中构建的spark镜像,注意,镜像是重中之重。由于公司内网无法访问gIThub, 镜像无法下载,需要自己构建。
为了方便管理,新建一个namespace, namespace-spark-cluster.yam
apiVersion: v1kind: Namespacemetadata: name: "spark-cluster" labels: name: "spark-cluster"
kubectl create -f namespace-spark-cluster.yaml 新建一个名为spark-cluster的namespace。
kubectl create -f namespace-spark-cluster.yaml
master分为两个部分,一个是类型为rc的主体,命名为spark-master-controller.yaml,另一部分为一个service,暴露master的端口给slaver使用(spark-master-service.yaml)。
spark-master-controller.yaml
apiVersion: v1kind: Namespacemetadata: name: "spark-cluster" labels: name: "spark-cluster"[root@iZ2ze48olpbvnopfiQQk33Z spark-cluster]# cat spark-master-service.yamlkind: ServiceapiVersion: v1metadata: name: spk-master namespace: spark-clustersPEc: ports: - port: 7077 targetPort: 7077 name: spark - port: 8080 targetPort: 8080 name: http selector: component: spk-master
以上为controller,直接使用spark的start-master脚本启动,但是启动后他会退到后台,导致k8s启动不了pod,所以还加了个tail -f一个master输出的LOG,顺便也方便查看log。
spark-master-service.yaml
kind: ServiceapiVersion: v1metadata: name: spk-master namespace: spark-clusterspec: ports: - port: 7077 targetPort: 7077 name: spark - port: 8080 targetPort: 8080 name: http selector: component: spk-master[root@iZ2ze48olpbvnopfiqqk33Z spark-cluster]# cat spark-master-controller.yamlkind: ReplicationControllerapiVersion: v1metadata: name: spark-master-controller namespace: spark-clusterspec: replicas: 1 selector: component: spk-master template: metadata: labels: component: spk-master spec: containers: - name: spk-master image: registry-vpc.cn-beijing.aliyuncs.com/acs/spark:spark-v2.4.5 imagePullpolicy: IfNotPResent command: ["/bin/sh"] args: ["-c","sh /opt/spark/sbin/start-master.sh && tail -f /opt/spark/logs/spark--org.apache.spark.deploy.master.Master-1-*"] ports: - containerPort: 7077 - containerPort: 8080 resources: requests: cpu: 100m
一个service,把7077端口和8080端口暴露出来给集群,方便slaver直接用spk-master:8080这样的方式进行访问。注意,只是暴露给集群,外部访问的方式最后会说。
kubectl create -f spark-master-controller.yaml
kubectl create -f spark-master-service.yaml
这里有个坑,start-master这个启动脚本中会用到SPARK_MASTER_PORT这个参数,而上边这个service如果名字为spark-master的话刚好冲突了,会把SPARK_MASTER_PORT设置为 host:port的形式,导致脚本启动失败。所以我一股脑把所有的spark-master改成spk-master了
启动worker脚本中需要传入master的地址,因为有dns且设置了service的缘故,可以通过spk-master.spark-cluster访问。把replicas设置为N即可启动N个worker。-- 另外,我还在worker上加了资源的限制,限制最多使用2个cpu以及12g内存。
spark-worker-controller.yaml
kind: ReplicationControllerapiVersion: v1metadata: name: spark-worker-controller namespace: spark-clusterspec: replicas: 3 selector: component: spark-worker template: metadata: labels: component: spark-worker spec: containers: - name: spark-worker image: registry-vpc.cn-beijing.aliyuncs.com/acs/spark:spark-v2.4.5 command: ["/bin/sh"] args: ["-c","sh /opt/spark/sbin/start-slave.sh spark://spk-master.spark-cluster:7077;tail -f /opt/spark/logs/spark--org.apache.spark.deploy.worker.Worker*"] ports: - containerPort: 8081 resources: requests: cpu: "1" memory: "2Gi"
image为elsonrodriguez/spark-ui-proxy:1.0 这玩意在一般启动standalone集群的时候是没有的,但是在k8s集群里边,则必不可缺。
设想一下,如果只是简单的暴露master的8080端口出来,我们只能看到master的管理页面,但是进一步从master访问worker的ui则变得不太现实(每个worker都有自己的ui地址,且ip分配很随机,这些ip只能在集群内部访问)。所以我们需要一个代理服务,从内部访问完我们需要的页面后,返回给我们,这样我们只需要暴露一个代理的地址即可。
kind: ReplicationControllerapiVersion: v1metadata: name: spark-ui-proxy-controllerspec: replicas: 1 selector: component: spark-ui-proxy template: metadata: labels: component: spark-ui-proxy spec: containers: - name: spark-ui-proxy image: elsonrodriguez/spark-ui-proxy:1.0 ports: - containerPort: 80 resources: requests: cpu: 100m args: - spk-master:8080 livenessProbe: httpGet: path: / port: 80 initialDelaySeconds: 120 timeoutSeconds: 5
kubectl create -f spark-ui-proxy-controller.yaml —namespace=spark-cluster
并且暴露proxy的80端口
kind: ServiceapiVersion: v1metadata: name: spark-ui-proxyspec: type: NodePort ports: - port: 80 targetPort: 80 nodePort: 32180 selector: component: spark-ui-proxy
kubectl create -f spark-ui-proxy-service.yaml —namespace=spark-cluster
至此,集群搭建完毕。可以通过集群的32180端口访问管理页面。
从git上下载依赖包,https://github.com/pingcap/tispark/releases ,随机选了一个releases的版本。
由于本地网络限制,无法访问github, 从服务器直接下载的
curl -LO https://github.com/pingcap/tispark/releases/tispark-asSEMbly-2.3.16.jar
使用第二步里的镜像,将依赖包打包进镜像里面
编写dockerfile
From registry-vpc.cn-beijing.aliyuncs.com/regis-k/vicky:spark-push-v2.4.5MAINTAINER mengqiwei@gwm.cnCOPY . /opt/spark/jars/
注意:Dockerfile与依赖包在同个目录下,并且打包的目录只有这两个文件
[root@iZ2ze48olpbvnopfiqqk33Z spark-images]# lsDockerfile tispark-assembly-2.3.16.jar
执行命令构建镜像
docker build -t tispark-2.3.16:spark-v2.4.5 .
注意:
更新namespace与tidb集群在同一个namespace里,免去再次配置网络的步骤。
更新镜像源 image: tispark-2.3.16:spark-v2.4.5
kind: ReplicationControllerapiVersion: v1metadata: name: spark-master-controller namespace: tidb-prodspec: replicas: 1 selector: component: spk-master template: metadata: labels: component: spk-master spec: containers: - name: spk-master image: tispark-2.3.16:spark-v2.4.5 imagePullPolicy: IfNotPresent command: ["/bin/sh"] args: ["-c","sh /opt/spark/sbin/start-master.sh && tail -f /opt/spark/logs/spark--org.apache.spark.deploy.master.Master-1-*"] ports: - containerPort: 7077 - containerPort: 8080 resources: requests: cpu: 100m
启动应用
kubectl apply -f spark-master-controller.yaml
kind: ServiceapiVersion: v1metadata: name: spk-master namespace: tidb-prodspec: ports: - port: 7077 targetPort: 7077 name: spark - port: 8080 targetPort: 8080 name: http selector: component: spk-master
启动应用
kubectl apply -f spark-master-service.yaml
kind: ReplicationControllerapiVersion: v1metadata: name: spark-worker-controller namespace: tidb-prodspec: replicas: 3 selector: component: spark-worker template: metadata: labels: component: spark-worker spec: containers: - name: spark-worker image: tispark-2.3.16:spark-v2.4.5 command: ["/bin/sh"] args: ["-c","sh /opt/spark/sbin/start-slave.sh spark://spk-master.spark-cluster:7077;tail -f /opt/spark/logs/spark--org.apache.spark.deploy.worker.Worker*"] ports: - containerPort: 8081 resources: requests: cpu: "1" memory: "2Gi"
启动应用
kubectl apply -f spark-worker-controller.yaml
查看运行状态
kubectl get all -n tidb-prod
登录到容器内
kubectl exec -it pod/spark-master-controller-9vrzw /bin/bash -n tidb-prod
在/opt/spark目录下运行命令, spark.tispark.pd.addresses的参数传入的是tidb的Pd所在的地址,可以通过kubectl get all -o wide查看地址信息
./bin/spark-shell --conf spark.tispark.pd.addresses=10.31.6.250:2379,10.31.6.2:2379,10.31.6.251:2379 --conf spark.SQL.extensions=org.apache.spark.sql.TiExtensions
执行命令显示结果,此处有个警告,暂未解决。
scala> spark.sql("show tables").show21/11/03 02:41:08 WARN ObjectStore: Failed to get database ods_cycle, returning NoSuchObjectException+---------+---------------+-----------+| database| tableName|isTemporary|+---------+---------------+-----------+|ods_cycle| ods_cycle_can| false||ods_cycle|ods_cycle_can_p| false||ods_cycle| test_cycle_can| false|+---------+---------------+-----------+
scala> spark.sql("select count(*) from test_cycle_can").show21/11/03 02:43:27 WARN ObjectStore: Failed to get database ods_cycle, returning NoSuchObjectException21/11/03 02:43:27 WARN ObjectStore: Failed to get database ods_cycle, returning NoSuchObjectException+--------+|count(1)|+--------+| 1410701|+--------+
以上是脚本宝典为你收集整理的Spark部署到K8S集群--standalone全部内容,希望文章能够帮你解决Spark部署到K8S集群--standalone所遇到的问题。
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