spark任务提交之SparkLauncher

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.csdn.net/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

遇到的问题，sparkLauncher一直运行不了；

这时hadoop，jdk都用了很长时间，排除其原因；

本地可以编写和运行scala，应该也不属于其中的问题；

最后发现cmd运行sparkin下的spark-submit会出现问题。于是重新拷贝linux下的spark包；

发现spark-shell可以正常运行，原来会报错：不是内部或外部命令，也不是可运行的程序或批处理文件

现在还存在的问题：

打jar包时，会有部分类打不进去，报错信息类没有找到；

等UI做成后，会更新整个流程。