spark任务提交之SparkLauncher

最新推荐文章于 2024-05-21 14:54:56 发布

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最新推荐文章于 2024-05-21 14:54:56 发布

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文章标签：大数据操作系统 scala

原文链接：http://www.cnblogs.com/parent-absent-son/p/10060364.html

本文详细介绍了如何使用SparkLauncher在Linux和Windows环境下运行Spark程序，包括配置环境参数、设置主类和资源路径，以及常见问题解决方法。

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最近需要做一个UI，在UI上做一个可以提交的spark程序的功能；

1-zeppelin就是这样的一个工具，其内部也是比较繁琐的。有兴趣的可以了解下。

2-SparkLauncher，spark自带的类

linux下其基本用法：

    public static void main(String[] args) throws Exception {
        HashMap<String, String> envParams = new HashMap<>();
        envParams.put("YARN_CONF_DIR", "/home/hadoop/cluster/hadoop-release/etc/hadoop");
        envParams.put("HADOOP_CONF_DIR", "/home/hadoop/cluster/hadoop-release/etc/hadoop");
        envParams.put("SPARK_HOME", "/home/hadoop/cluster/spark-new");
        envParams.put("SPARK_PRINT_LAUNCH_COMMAND", "1");

        SparkAppHandle spark = new SparkLauncher(envParams)
                .setAppResource("/home/hadoop/cluster/spark-new/examples/jars/spark-examples_2.11-2.2.1.jar")
                .setMainClass("org.apache.spark.examples.SparkPi")
                .setMaster("yarn")
                .startApplication();

        Thread.sleep(100000);
    }

运行结果：

信息: 18/12/03 18:12:12 INFO scheduler.DAGScheduler: ResultStage 0 (reduce at SparkPi.scala:38) finished in 1.462 s
十二月 03, 2018 6:12:12 下午 org.apache.spark.launcher.OutputRedirector redirect
信息: 18/12/03 18:12:12 INFO scheduler.DAGScheduler: Job 0 finished: reduce at SparkPi.scala:38, took 3.395705 s
十二月 03, 2018 6:12:12 下午 org.apache.spark.launcher.OutputRedirector redirect
信息: Pi is roughly 3.1461157305786527

windows下运行：

如果linux能运行，那就安装windows下所依赖包，包含jdk，hadoop，scala，spark;

可以参考https://blog.youkuaiyun.com/u011513853/article/details/52865076

代码贴上：

public class SparkLauncherTest {
    private static String YARN_CONF_DIR = null;
    private static String HADOOP_CONF_DIR = null;
    private static String SPARK_HOME = null;
    private static String SPARK_PRINT_LAUNCH_COMMAND = "1";
    private static String Mater = null;
    private static String appResource = null;
    private static String mainClass = null;

    public static void main(String[] args) throws Exception {
        if (args.length != 1) {
            System.out.println("Usage: ServerStatisticSpark <local>");
            System.exit(1);
        }

        TrackerConfig trackerConfig = TrackerConfig.loadConfig();

        if ("local".equals(args[0])){
            YARN_CONF_DIR="D:\\software\\hadoop-2.4.1\\etc\\hadoop";
            HADOOP_CONF_DIR="D:\\software\\hadoop-2.4.1\\etc\\hadoop";
            SPARK_HOME="D:\\spark-new";
            Mater = "local";
            appResource = "D:\\spark-new\\examples\\jars\\spark-examples_2.11-2.2.1.jar";
        } else {
            YARN_CONF_DIR="/home/hadoop/cluster/hadoop-release/etc/hadoop";
            HADOOP_CONF_DIR="/home/hadoop/cluster/hadoop-release/etc/hadoop";
            SPARK_HOME="/home/hadoop/cluster/spark-new";
            Mater = "yarn";
            appResource = "/home/hadoop/cluster/spark-new/examples/jars/spark-examples_2.11-2.2.1.jar";
        }

        HashMap<String, String> envParams = new HashMap<>();
        envParams.put("YARN_CONF_DIR", YARN_CONF_DIR);
        envParams.put("HADOOP_CONF_DIR", HADOOP_CONF_DIR);
        envParams.put("SPARK_HOME", SPARK_HOME);
        envParams.put("SPARK_PRINT_LAUNCH_COMMAND", SPARK_PRINT_LAUNCH_COMMAND);

        mainClass = "org.apache.spark.examples.SparkPi";
        SparkAppHandle spark = new SparkLauncher(envParams)
                .setAppResource(appResource)
                .setMainClass(mainClass)
                .setMaster(Mater)
                .startApplication();
        Thread.sleep(100000);
    }

}

运行结果：

信息: 18/12/04 17:01:11 INFO scheduler.DAGScheduler: Job 0 finished: reduce at SparkPi.scala:38, took 0.808691 s
十二月 04, 2018 5:01:11 下午 org.apache.spark.launcher.OutputRedirector redirect
信息: Pi is roughly 3.1455757278786396