The clusters-submiter from zck573693104

IDEA直接提交Flink/Spark任务到集群，进行快速开发调试。任务提交代码稍加改造后可以和上层调度系统进行集成。

任务类型提交入口类：

Flink 多执行模式任务提交

需要填写Flink任务运行时参数配置，任务运行所在的集群配置路径，本地Flink根路径。项目依赖flink1.10版本。
支持以YarnSession、YarnPerjob、Standalone模式进行任务提交，返回ApplicationId。
example模块下包含一个FlinkDemo，打包后会转移到项目的examplJars中，可以尝试进行任务提交。
任务提交后，根据ApplicationId获取任务执行使用的jm、tm日志基本信息，包含日志访问URL,日志总字节大小,根据日志基本信息可以做日志滚动展示，防止Yarn日志过大导致日志读取卡死。

任务提交示例：

   // 可执行jar包路径
    String runJarPath = "/Users/maqi/code/ClustersSubmiter/exampleJars/flink-kafka-reader/flink-kafka-reader.jar";
    // 任务参数
    String[] execArgs = new String[]{"-jobName", "flink110Submit", "--topic", "mqTest01", "--bootstrapServers", "172.16.8.107:9092"};
    // 任务名称
    String jobName = "Flink perjob submit";
    // flink 文件夹路径
    String flinkConfDir = "/Users/maqi/tmp/flink/flink-1.10.0/conf";
    // flink lib包路径
    String flinkJarPath = "/Users/maqi/tmp/flink/flink-1.10.0/lib";
    //  yarn 文件夹路径
    //        String yarnConfDir = "/Users/maqi/tmp/hadoopconf";
    String yarnConfDir = "/Users/maqi/tmp/hadoopconf";
    //  作业依赖的外部文件，例如：udf jar , keytab
    String[] dependFile = new String[]{"/Users/maqi/tmp/flink/flink-1.10.0/README.txt"};
    // 任务提交队列
    String queue = "root.users.hdfs";
    // flink任务执行模式
    String execMode = "yarnPerjob";
    // yarnsession appid配置
    Properties yarnSessionConfProperties = null;
    // savepoint 及并行度相关
    Properties confProperties = new Properties();
    confProperties.setProperty("parallelism", "1");

  JobParamsInfo jobParamsInfo = JobParamsInfo.builder()
            .setExecArgs(execArgs)
            .setName(jobName)
            .setRunJarPath(runJarPath)
            .setDependFile(dependFile)
            .setFlinkConfDir(flinkConfDir)
            .setYarnConfDir(yarnConfDir)
            .setConfProperties(confProperties)
            .setYarnSessionConfProperties(yarnSessionConfProperties)
            .setFlinkJarPath(flinkJarPath)
            .setQueue(queue)
            .build();


   String applicationId = runFlinkJob(jobParamsInfo, execMode);
   List<String> logsInfo = new RunningLog().getRollingLogBaseInfo(jobParamsInfo, applicationId);
   logsInfo.forEach(System.out::println);

jobmanager日志格式:

{
   "logs":[
       {
           "name":"taskmanager.err ",
           "totalBytes":"560",
           "url":"http://node03:8042/node/containerlogs/container_e27_1593571725037_0170_01_000002/admin/taskmanager.err/"
       },
       {
           "name":"taskmanager.log ",
           "totalBytes":"35937",
           "url":"http://node03:8042/node/containerlogs/container_e27_1593571725037_0170_01_000002/admin/taskmanager.log/"
       },
       {
           "name":"taskmanager.out ",
           "totalBytes":"0",
           "url":"http://node03:8042/node/containerlogs/container_e27_1593571725037_0170_01_000002/admin/taskmanager.out/"
       }
   ],
   "otherInfo":"{"dataPort":36218,"freeSlots":0,"hardware":{"cpuCores":4,"freeMemory":241172480,"managedMemory":308700779,"physicalMemory":8201641984},"id":"container_e27_1593571725037_0170_01_000002","path":"akka.tcp://flink@node03:36791/user/taskmanager_0","slotsNumber":1,"timeSinceLastHeartbeat":1593659561129}",
   "typeName":"taskmanager"
}

taskmanager日志格式:

{
    "logs":[
        {
            "name":"taskmanager.err ",
            "totalBytes":"560",
            "url":"http://node03:8042/node/containerlogs/container_e27_1593571725037_0170_01_000002/admin/taskmanager.err/"
        },
        {
            "name":"taskmanager.log ",
            "totalBytes":"35937",
            "url":"http://node03:8042/node/containerlogs/container_e27_1593571725037_0170_01_000002/admin/taskmanager.log/"
        },
        {
            "name":"taskmanager.out ",
            "totalBytes":"0",
            "url":"http://node03:8042/node/containerlogs/container_e27_1593571725037_0170_01_000002/admin/taskmanager.out/"
        }
    ],
    "otherInfo":"{"dataPort":36218,"freeSlots":0,"hardware":{"cpuCores":4,"freeMemory":241172480,"managedMemory":308700779,"physicalMemory":8201641984},"id":"container_e27_1593571725037_0170_01_000002","path":"akka.tcp://flink@node03:36791/user/taskmanager_0","slotsNumber":1,"timeSinceLastHeartbeat":1593659561129}",
    "typeName":"taskmanager"
}

Spark on yarn 任务提交

填写用户程序包路径、执行参数、集群配置文件夹、安全认证等相关配置。
Spark任务提交使用Yarn cluster模式，使用的Spark Jar需要提前上传到HDFS并作为archive的参数。
针对SparkSQL任务，通过提交examples中的spark-sql-proxy程序包来直接操作hive表。

提交示例：

public static void main(String[] args) throws Exception {
        boolean openKerberos = true;
        String appName = "todd spark submit";
        String runJarPath = "/Users/maqi/code/ClustersSubmiter/exampleJars/spark-sql-proxy/spark-sql-proxy.jar";
        String mainClass = "cn.todd.spark.SparksqlProxy";
        String yarnConfDir = "/Users/maqi/tmp/hadoopconf";
        String principal = "hdfs/[email protected]";
        String keyTab = "/Users/maqi/tmp/hadoopconf/hdfs.keytab";
        String jarHdfsDir = "sparkproxy2";
        String archive = "hdfs://nameservice1/sparkjars/jars";
        String queue = "root.users.hdfs";
        String execArgs = getExampleJobParams();

        Properties confProperties = new Properties();
        confProperties.setProperty("spark.executor.cores","2");

        JobParamsInfo jobParamsInfo = JobParamsInfo.builder()
                .setAppName(appName)
                .setRunJarPath(runJarPath)
                .setMainClass(mainClass)
                .setYarnConfDir(yarnConfDir)
                .setPrincipal(principal)
                .setKeytab(keyTab)
                .setJarHdfsDir(jarHdfsDir)
                .setArchivePath(archive)
                .setQueue(queue)
                .setExecArgs(execArgs)
                .setConfProperties(confProperties)
                .setOpenKerberos(BooleanUtils.toString(openKerberos, "true", "false"))
                .build();

        YarnConfiguration yarnConf = YarnConfLoaderUtil.getYarnConf(yarnConfDir);
        String applicationId = "";

        if (BooleanUtils.toBoolean(openKerberos)) {
            UserGroupInformation.setConfiguration(yarnConf);
            UserGroupInformation userGroupInformation = UserGroupInformation.loginUserFromKeytabAndReturnUGI(principal, keyTab);
            applicationId = userGroupInformation.doAs((PrivilegedExceptionAction<String>) () -> LauncherMain.run(jobParamsInfo, yarnConf));
        } else {
            LauncherMain.run(jobParamsInfo, yarnConf);
        }

        System.out.println(applicationId);
    }

Spark on k8s 任务提交

基于Spark2.4.4进行开发，通过将spark-sql-proxy.jar包打入镜像来执行Sparksql并操作Hive表，无其他特殊操作。
操作Hive时需要传递hadoopConfDir,程序会自动将.xml文件内容进行挂载，如果非root用户操作Hive,需要设置HADOOP_USER_NAME。
通过读取kubeConfig配置文件进行Kuberclient的创建,而非官方提供的master url方式。
任务提交后立即返回spark-app-selector id,从而进行POD状态获取。

  public static void main(String[] args) throws Exception {
      String appName = "todd spark submit";
      // 镜像内的jar路径
      String runJarPath = "local:///opt/dtstack/spark/spark-sql-proxy.jar";
      String mainClass = "cn.todd.spark.SparksqlProxy";
      String hadoopConfDir = "/Users/maqi/tmp/hadoopconf/";
      String kubeConfig = "/Users/maqi/tmp/conf/k8s.config";
      String imageName = "mqspark:2.4.4";
      String execArgs = getExampleJobParams();

      Properties confProperties = new Properties();
      confProperties.setProperty("spark.executor.instances", "2");
      confProperties.setProperty("spark.kubernetes.namespace", "default");
      confProperties.setProperty("spark.kubernetes.authenticate.driver.serviceAccountName", "spark");
      confProperties.setProperty("spark.kubernetes.container.image.pullPolicy", "IfNotPresent");


      JobParamsInfo jobParamsInfo = JobParamsInfo.builder()
              .setAppName(appName)
              .setRunJarPath(runJarPath)
              .setMainClass(mainClass)
              .setExecArgs(execArgs)
              .setConfProperties(confProperties)
              .setHadoopConfDir(hadoopConfDir)
              .setKubeConfig(kubeConfig)
              .setImageName(imageName)
              .build();

      String id = run(jobParamsInfo);
      System.out.println(id);
  }

zck573693104 / clusters-submiter Goto Github PK

clusters-submiter's Introduction

Flink 多执行模式任务提交

Spark on yarn 任务提交

Spark on k8s 任务提交

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