Spark2.3（四十）：如何使用java通过yarn api调度spark app，并根据appId监控任务，关闭任务，获取任务日志

背景：

调研过OOZIE和AZKABA,这种都是只是使用spark-submit.sh来提交任务，任务提交上去之后获取不到ApplicationId，更无法跟踪spark application的任务状态，无法kill application，更无法获取application的日志信息。因此，为了实现一个spark的调度平台所以有了以下调研及测试结论。

调研目前流行的SPARK任务调度：Oozie和Azkaban。

但是这两个平台不能满足以下功能（这些功能是希望有的）：

1) 无法满足即安全（使用shell提交任务，操作用户权限控制）又可以Spark状态监控（跟踪SPARK application的任务状态）；

2) 无法满足监控集群运行状态；

3) 无法满足对每个任务设置监控策略。比如：任务假死状态判定。

一个合格的spark调度平台要具有的基本功能：可以submit，kill，监控，获取日志，跟踪历史记录。

本篇文章主要讲解如何使用YarnClient API实现，借助于YarnClient来实现监控任务，杀死任务，获取日志，使用org.apache.spark.deploy.yarn.Client提交spark任务并返回spark任务的applicationId。

备注：之前研究过使用SparkLauncher类进行调度，该方案也是一种不错的方案，如果读者你喜欢也可以尝试使用SparkLauncher，它一样可以提交后返回spark任务的applicationid（提交后无状态，需要等待applicaitonId不为空为止）。

环境配置：

1）由于我们是使用java 代码（需要发布到web项目中，而不是shell调用[不可以再shell中设置环境变量]）去调用，因此我们需要centos系统环境变量中包含以下变量：

SPARK_KAFKA_VERSION
HADOOP_HOME
HADOOP_COMMON_HOME
SPARK_HOME

SPARK_CONF_DIR
HADOOP_CONF_DIR
YARN_CONF_DIR

SPARK_DIST_CLASSPATH
SPARK_EXTRA_LIB_PATH
LD_LIBRARY_PATH

如果你对spark-env.sh文件比较熟悉的话，你会发现上边这些变量来自于该文件，那么，我们嗯只需要把spark-env.sh引入到/ect/profile就可以。

spark-env.sh

 bash-4.1$ more /home1/opt/cloudera/parcels/SPARK2-2.3.0.cloudera3-1.cdh5.13.3.p0.458809/lib/spark2/conf/spark-env.sh
 #!/usr/bin/env bash
 ##
 # Generated by Cloudera Manager and should not be modified directly
 ##

 SELF="$(cd $(dirname $BASH_SOURCE) && pwd)"
 if [ -z "$SPARK_CONF_DIR" ]; then
   export SPARK_CONF_DIR="$SELF"
 fi

 export SPARK_HOME=/home1/opt/cloudera/parcels/SPARK2-2.3.0.cloudera3-1.cdh5.13.3.p0.458809/lib/spark2

 SPARK_PYTHON_PATH=""
 if [ -n "$SPARK_PYTHON_PATH" ]; then
   export PYTHONPATH="$PYTHONPATH:$SPARK_PYTHON_PATH"
 fi

 export HADOOP_HOME=/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/lib/hadoop
 export HADOOP_COMMON_HOME="$HADOOP_HOME"

 if [ -n "$HADOOP_HOME" ]; then
   LD_LIBRARY_PATH=$LD_LIBRARY_PATH:${HADOOP_HOME}/lib/native
 fi

 SPARK_EXTRA_LIB_PATH="/home1/opt/cloudera/parcels/GPLEXTRAS-5.13.0-1.cdh5.13.0.p0.29/lib/hadoop/lib/native"
 if [ -n "$SPARK_EXTRA_LIB_PATH" ]; then
   LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$SPARK_EXTRA_LIB_PATH
 fi

 export LD_LIBRARY_PATH

 HADOOP_CONF_DIR=${HADOOP_CONF_DIR:-$SPARK_CONF_DIR/yarn-conf}
 export HADOOP_CONF_DIR

 PYLIB="$SPARK_HOME/python/lib"
 if [ -f "$PYLIB/pyspark.zip" ]; then
   PYSPARK_ARCHIVES_PATH=
   for lib in "$PYLIB"/*.zip; do
     if [ -n "$PYSPARK_ARCHIVES_PATH" ]; then
       PYSPARK_ARCHIVES_PATH="$PYSPARK_ARCHIVES_PATH,local:$lib"
     else
       PYSPARK_ARCHIVES_PATH="local:$lib"
     fi
   done
   export PYSPARK_ARCHIVES_PATH
 fi

 # Spark uses `set -a` to export all variables created or modified in this
 # script as env vars. We use a temporary variables to avoid env var name
 # collisions.
 # If PYSPARK_PYTHON is unset, set to CDH_PYTHON
 TMP_PYSPARK_PYTHON=${PYSPARK_PYTHON:-''}
 # If PYSPARK_DRIVER_PYTHON is unset, set to CDH_PYTHON
 TMP_PYSPARK_DRIVER_PYTHON=${PYSPARK_DRIVER_PYTHON:-}

 if [ -n "$TMP_PYSPARK_PYTHON" ] && [ -n "$TMP_PYSPARK_DRIVER_PYTHON" ]; then
   export PYSPARK_PYTHON="$TMP_PYSPARK_PYTHON"
   export PYSPARK_DRIVER_PYTHON="$TMP_PYSPARK_DRIVER_PYTHON"
 fi

 # Add the Kafka jars configured by the user to the classpath.
 SPARK_DIST_CLASSPATH=
 SPARK_KAFKA_VERSION=${SPARK_KAFKA_VERSION:-'0.10'}
 case "$SPARK_KAFKA_VERSION" in
   0.9)
     SPARK_DIST_CLASSPATH="$SPARK_HOME/kafka-0.9/*"
     ;;
   0.10)
     SPARK_DIST_CLASSPATH="$SPARK_HOME/kafka-0.10/*"
     ;;
   None)
     ;;
   *)
     echo "Invalid Kafka version: $SPARK_KAFKA_VERSION"
     exit 1
     ;;
 esac

 export SPARK_DIST_CLASSPATH="$SPARK_DIST_CLASSPATH:$(paste -sd: "$SELF/classpath.txt")"

接下来在/ect/profile文件最后一样追加

source /home1/opt/cloudera/parcels/SPARK2-2.3..cloudera3-.cdh5.13.3.p0./lib/spark2/conf/spark-env.sh

，保存，然后source /etc/profile使其生效。

2）需要修改yarn上传资源文件存储位置，否则会出现错误找不到资源文件（文件之所以找不到，是因为那些资源文件spark_lib.zip,spark_conf.zip,*.jar被上传到本地的/curent_user[root、zhangsan、lisi]/.sparkStaging/{appId}/*.jar下，在其他executor|container上找不到），必须修改yarn资源文件上传到hdfs目录下：

第一步：提交任务代码中设置SparkConf变量：

sparkConf.set("spark.yarn.stagingDir", "hdfs://vm192.168.0.141.com.cn:8020/user/");

第二步：手动创建hdfs目录 /user/.sparkStaging，给分配权限：

bash-4.1$ sudo -uhdfs hadoop fs -mkdir /user/.sparkStaging
bash-4.1$ sudo -uhdfs hadoop fs -chown zhangsan:zhangsan /user/.sparkStaging

第三步：导入pom.xml依赖包

    <properties>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
        <es.version>6.4.2</es.version>
        <spark.version>2.3.0</spark.version>
        <scala.version>2.11</scala.version>
    </properties>

    <dependencies>
        <!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-yarn-client -->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-yarn-client</artifactId>
            <version>2.6.5</version>
        </dependency>

        <!--Spark -->
        <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-yarn -->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-yarn_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql-kafka-0-10_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming-kafka-0-10_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>

        <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-launcher -->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-launcher_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka_2.11</artifactId>
            <version>0.10.0.1</version>
        </dependency>
        <dependency>
            <groupId>org.scala-lang</groupId>
            <artifactId>scala-library</artifactId>
            <version>2.11.6</version>
       </dependency>
        <dependency>
            <groupId>com.twitter</groupId>
            <artifactId>bijection-avro_${scala.version}</artifactId>
            <version>0.9.5</version>
        </dependency>
        <dependency>
            <groupId>com.databricks</groupId>
            <artifactId>spark-avro_${scala.version}</artifactId>
            <version>3.2.0</version>
            <type>jar</type>
        </dependency>

        <dependency>
            <groupId>org.elasticsearch</groupId>
            <artifactId>elasticsearch-spark-20_${scala.version}</artifactId>
            <version>${es.version}</version>
        </dependency>
        <dependency>
            <groupId>org.elasticsearch.client</groupId>
            <artifactId>transport</artifactId>
            <version>${es.version}</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/com.alibaba/fastjson -->
        <dependency>
            <groupId>com.alibaba</groupId>
            <artifactId>fastjson</artifactId>
            <version>1.2.54</version>
        </dependency>

        <dependency>
            <groupId>junit</groupId>
            <artifactId>junit</artifactId>
            <version>3.8.1</version>
            <scope>test</scope>
        </dependency>
    </dependencies>

spark提交任务：

参数类YarnSubmitConditions：

 import java.util.List;
 import java.util.Map;

 public class YarnSubmitConditions {
     private List<String> otherArgs;
     private String applicationJar;
     private String mainClass;
     private String appName;
     private String[] additionalJars;
     private String sparkYarnJars;
     public String[] files;
     public String yarnResourcemanagerAddress;
     public String sparkFsDefaultFS;
     private String driverMemory;
     private String numExecutors;
     private String executorMemory;
     private String executorCores;
     private String sparkHome;
     private String deployMode;
     private String master;
     public Map<String, String> sparkProperties;

     public List<String> getOtherArgs() {
         return otherArgs;
     }

     public void setOtherArgs(List<String> otherArgs) {
         this.otherArgs = otherArgs;
     }

     public String getApplicationJar() {
         return applicationJar;
     }

     public void setApplicationJar(String applicationJar) {
         this.applicationJar = applicationJar;
     }

     public String getMainClass() {
         return mainClass;
     }

     public void setMainClass(String mainClass) {
         this.mainClass = mainClass;
     }

     public String getAppName() {
         return appName;
     }

     public void setAppName(String appName) {
         this.appName = appName;
     }

     public String[] getAdditionalJars() {
         return additionalJars;
     }

     public void setAdditionalJars(String[] additionalJars) {
         this.additionalJars = additionalJars;
     }

     public String getSparkYarnJars() {
         return sparkYarnJars;
     }

     public void setSparkYarnJars(String sparkYarnJars) {
         this.sparkYarnJars = sparkYarnJars;
     }

     public String[] getFiles() {
         return files;
     }

     public void setFiles(String[] files) {
         this.files = files;
     }

     public String getYarnResourcemanagerAddress() {
         return yarnResourcemanagerAddress;
     }

     public void setYarnResourcemanagerAddress(String yarnResourcemanagerAddress) {
         this.yarnResourcemanagerAddress = yarnResourcemanagerAddress;
     }

     public Map<String, String> getSparkProperties() {
         return sparkProperties;
     }

     public void setSparkProperties(Map<String, String> sparkProperties) {
         this.sparkProperties = sparkProperties;
     }

     public String getSparkFsDefaultFS() {
         return sparkFsDefaultFS;
     }

     public void setSparkFsDefaultFS(String sparkFsDefaultFS) {
         this.sparkFsDefaultFS = sparkFsDefaultFS;
     }

     public String getDriverMemory() {
         return driverMemory;
     }

     public void setDriverMemory(String driverMemory) {
         this.driverMemory = driverMemory;
     }

     public String getNumExecutors() {
         return numExecutors;
     }

     public void setNumExecutors(String numExecutors) {
         this.numExecutors = numExecutors;
     }

     public String getExecutorMemory() {
         return executorMemory;
     }

     public void setExecutorMemory(String executorMemory) {
         this.executorMemory = executorMemory;
     }

     public String getExecutorCores() {
         return executorCores;
     }

     public void setExecutorCores(String executorCores) {
         this.executorCores = executorCores;
     }

     public String getSparkHome() {
         return sparkHome;
     }

     public void setSparkHome(String sparkHome) {
         this.sparkHome = sparkHome;
     }

     public String getDeployMode() {
         return deployMode;
     }

     public void setDeployMode(String deployMode) {
         this.deployMode = deployMode;
     }

     public String getMaster() {
         return master;
     }

     public void setMaster(String master) {
         this.master = master;
     }
 }

提交函数：

    /**
     * 提交任务到yarn集群
     *
     * @param conditions
     *            yarn集群，spark，hdfs具体信息，参数等
     * @return appid
     */
    public static String submitSpark(YarnSubmitConditions conditions) {
        logger.info("初始化spark on yarn参数");
        // 初始化yarn客户端
        logger.info("初始化spark on yarn客户端");

        List<String> args = Lists.newArrayList(//
                "--jar", conditions.getApplicationJar(),//
                "--class", conditions.getMainClass()//
                );
        if (conditions.getOtherArgs() != null && conditions.getOtherArgs().size() > 0) {
            for (String s : conditions.getOtherArgs()) {
                args.add("--arg");
                args.add(org.apache.commons.lang.StringUtils.join(new String[] { s }, ","));
            }
        }

        // identify that you will be using Spark as YARN mode
        System.setProperty("SPARK_YARN_MODE", "true");

        System.out.println("SPARK_YARN_MODE:" + System.getenv("SPARK_YARN_MODE"));
        System.out.println("SPARK_CONF_DIR:" + System.getenv("SPARK_CONF_DIR"));
        System.out.println("HADOOP_CONF_DIR:" + System.getenv("HADOOP_CONF_DIR"));
        System.out.println("YARN_CONF_DIR:" + System.getenv("YARN_CONF_DIR"));
        System.out.println("SPARK_KAFKA_VERSION:" + System.getenv("SPARK_KAFKA_VERSION"));
        System.out.println("HADOOP_HOME:" + System.getenv("HADOOP_HOME"));
        System.out.println("HADOOP_COMMON_HOME:" + System.getenv("HADOOP_COMMON_HOME"));
        System.out.println("SPARK_HOME:" + System.getenv("SPARK_HOME"));
        System.out.println("SPARK_DIST_CLASSPATH:" + System.getenv("SPARK_DIST_CLASSPATH"));
        System.out.println("SPARK_EXTRA_LIB_PATH:" + System.getenv("SPARK_EXTRA_LIB_PATH"));
        System.out.println("LD_LIBRARY_PATH:" + System.getenv("LD_LIBRARY_PATH"));

        SparkConf sparkConf = new SparkConf();

        sparkConf.setSparkHome(conditions.getSparkHome());
        sparkConf.setMaster(conditions.getMaster());
        sparkConf.set("spark.submit.deployMode", conditions.getDeployMode());
        sparkConf.setAppName(conditions.getAppName());

        // --driver-memory
        sparkConf.set("spark.driver.memory", conditions.getDriverMemory());
        // --executor-memory
        sparkConf.set("spark.executor.memory", conditions.getExecutorMemory());
        // --executor-cores
        sparkConf.set("spark.executor.cores", conditions.getExecutorCores());
        // --num-executors
        sparkConf.set("spark.executor.instance", conditions.getNumExecutors());
        // The folder '.sparkStaging' will be created auto.
        // System.out.println("SPARK_YARN_STAGING_DIR:"+System.getenv("SPARK_YARN_STAGING_DIR"))
        sparkConf.set("spark.yarn.stagingDir", "hdfs://vm192.168.0.141.com.cn:8020/user/");
        // sparkConf.set("spark.jars",);
        // sparkConf.set("spark.yarn.jars", conditions.getSparkYarnJars());
        if (conditions.getAdditionalJars() != null && conditions.getAdditionalJars().length > 0) {
            sparkConf.set("spark.repl.local.jars", org.apache.commons.lang.StringUtils.join(conditions.getAdditionalJars(), ","));
            sparkConf.set("spark.yarn.dist.jars", org.apache.commons.lang.StringUtils.join(conditions.getAdditionalJars(), ","));
        }

        // "--files","hdfs://node1:8020/user/root/yarn-site.xml",
        if (conditions.getFiles() != null && conditions.getFiles().length > 0) {
            sparkConf.set("spark.files", org.apache.commons.lang.StringUtils.join(conditions.getFiles(), ","));
        }

        for (Map.Entry<String, String> e : conditions.getSparkProperties().entrySet()) {
            sparkConf.set(e.getKey().toString(), e.getValue().toString());
        }

        // mapred-site.xml
        // 指定使用yarn框架
        sparkConf.set("mapreduce.framework.name", "yarn");
        // 指定historyserver
        sparkConf.set("mapreduce.jobhistory.address", "vm192.168.0.141.com.cn:10020");

        // yarn-site.xml
        // 添加这个参数，不然spark会一直请求0.0.0.0:8030,一直重试
        sparkConf.set("yarn.resourcemanager.hostname", conditions.getYarnResourcemanagerAddress().split(":")[0]);
        // 指定资源分配器
        sparkConf.set("yarn.resourcemanager.scheduler.address", "vm192.168.0.141.com.cn:8030");
        // 设置为true，不删除缓存的jar包，因为现在提交yarn任务是使用的代码配置，没有配置文件，删除缓存的jar包有问题，
        sparkConf.set("spark.yarn.preserve.staging.files", "false");

        // spark2.2
        // 初始化 yarn的配置
        // Configuration cf = new Configuration();
        // String cross_platform = "false";
        // String os = System.getProperty("os.name");
        //     if (os.contains("Windows")) {
        //     cross_platform = "true";
        // }
        // 配置使用跨平台提交任务
        // cf.set("mapreduce.app-submission.cross-platform", cross_platform);
        // 设置yarn资源，不然会使用localhost:8032
        // cf.set("yarn.resourcemanager.address",
        // conditions.getYarnResourcemanagerAddress());
        // 设置namenode的地址，不然jar包会分发，非常恶心
        // cf.set("fs.defaultFS", conditions.getSparkFsDefaultFS());

        // spark2.2
        // Client client = new Client(cArgs, cf, sparkConf);
        // spark2.3
        ClientArguments cArgs = new ClientArguments(args.toArray(new String[args.size()]));
        org.apache.spark.deploy.yarn.Client client = new Client(cArgs, sparkConf);

        logger.info("提交任务，任务名称：" + conditions.getAppName());

        try {
            ApplicationId appId = client.submitApplication();
            return appId.toString();
        } catch (Exception e) {
            logger.error("提交spark任务失败", e);
            return null;
        } finally {
            if (client != null) {
                client.stop();
            }
        }
    }

测试函数

    private static final org.slf4j.Logger logger = org.slf4j.LoggerFactory.getLogger(TestSubmit.class);

    public static void main(String[] args) {
        YarnSubmitConditions conditions = new YarnSubmitConditions();
        conditions.setAppName("test yarn submit app");
        conditions.setMaster("yarn");
        conditions.setSparkHome("/home1/opt/cloudera/parcels/SPARK2/lib/spark2/");
        conditions.setDeployMode("cluster");
        conditions.setDriverMemory("3g");
        conditions.setExecutorMemory("3g");
        conditions.setExecutorCores("1");
        conditions.setNumExecutors("5");

        // /etc/hadoop/conf.cloudera.yarn/core-site.xml
        conditions.setYarnResourcemanagerAddress("vm192.168.0.141.com.cn:8032");
        // /etc/hadoop/conf.cloudera.yarn/yarn-site.xml
        conditions.setSparkFsDefaultFS("hdfs://vm192.168.0.141.com.cn:8020");
        conditions.setFiles(new String[] { "/etc/hadoop/conf.cloudera.yarn/hdfs-site.xml",//
                "/etc/hadoop/conf.cloudera.yarn/mapred-site.xml",//
                "/etc/hadoop/conf.cloudera.yarn/yarn-site.xml",//
        });
        conditions.setApplicationJar("/home1/zhangsan/mrs-streaming-driver.jar");
        conditions.setMainClass("com.boco.mrs.streaming.Main");
        conditions.setOtherArgs(Arrays.asList("RSRP", "TestBroadcastDriver"));
        List<String> sparkJars = getSparkJars("/home1/zhangsan/sparkjars/");
        conditions.setAdditionalJars(sparkJars.toArray(new String[sparkJars.size()]));

        Map<String, String> propertiesMap = null;
        try {
            propertiesMap = getSparkProperties("/home1/zhangsan/conf/spark-properties-mrs.conf");
        } catch (IOException e) {
            e.printStackTrace();
        }
        conditions.setSparkProperties(propertiesMap);

        String appId = submitSpark(conditions);

        System.out.println("application id is " + appId);
        System.out.println("Complete ....");
    }

    /**
     * 加载sparkjars下的jar文件
     * */
    private static List<String> getSparkJars(String dir) {
        List<String> items = new ArrayList<String>();

        File file = new File(dir);
        for (File item : file.listFiles()) {
            items.add(item.getPath());
        }

        return items;
    }

    /**
     * 加载spark-properties.conf配置文件
     * */
    private static Map<String, String> getSparkProperties(String filePath) throws IOException {
        Map<String, String> propertiesMap = new HashMap<String, String>();
        BufferedReader reader = new BufferedReader(new FileReader(filePath));
        String line = null;
        while ((line = reader.readLine()) != null) {
            if (line.trim().length() > 0 && !line.startsWith("#") && line.indexOf("=") != -1) {
                String[] fields = line.split("=");
                propertiesMap.put(fields[0], fields[1]);
            }
        }
        reader.close();

        return propertiesMap;
    }

测试函数执行脚本：

bash-4.1$ more test.sh
#/bin/sh
#LANG=zh_CN.utf8
#export LANG
export SPARK_KAFKA_VERSION=0.10
export LANG=zh_CN.UTF-

java -cp ./sparkjars/*:./mrs-streaming-driver.jar com.dx.mrs.streaming.batchmodule.TestSubmit

执行日志：

 bash-4.1$ ./test.sh
 log4j:WARN No appenders could be found for logger (com.dx.mrs.streaming.batchmodule.TestSubmit).
 log4j:WARN Please initialize the log4j system properly.
 log4j:WARN See http://logging.apache.org/log4j/1.2/faq.html#noconfig for more info.
 SPARK_YARN_MODE:null
 SPARK_CONF_DIR:/home1/opt/cloudera/parcels/SPARK2-2.3..cloudera3-.cdh5.13.3.p0./lib/spark2/conf
 HADOOP_CONF_DIR:/home1/opt/cloudera/parcels/SPARK2-2.3..cloudera3-.cdh5.13.3.p0./lib/spark2/conf/yarn-conf
 YARN_CONF_DIR:null
 SPARK_KAFKA_VERSION:0.10
 HADOOP_HOME:/home1/opt/cloudera/parcels/CDH-5.13.-.cdh5.13.0.p0./lib/hadoop
 HADOOP_COMMON_HOME:/home1/opt/cloudera/parcels/CDH-5.13.-.cdh5.13.0.p0./lib/hadoop
 SPARK_HOME:/home1/opt/cloudera/parcels/SPARK2-2.3..cloudera3-.cdh5.13.3.p0./lib/spark2
 SPARK_DIST_CLASSPATH:/home1/opt/cloudera/parcels/SPARK2-2.3..cloudera3-.cdh5.13.3.p0./lib/spark2/kafka-0.10/*:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/activation-1.1.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/aopalliance-1.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/apacheds-i18n-2.0.0-M15.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/apacheds-kerberos-codec-2.0.0-M15.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/api-asn1-api-1.0.0-M20.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/api-util-1.0.0-M20.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/asm-3.2.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/avro-1.7.6-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/aws-java-sdk-bundle-1.11.134.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/azure-data-lake-store-sdk-2.2.3.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/commons-beanutils-1.9.2.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/commons-beanutils-core-1.8.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/commons-codec-1.4.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/commons-configuration-1.6.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/commons-daemon-1.0.13.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/commons-digester-1.8.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/commons-el-1.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/commons-math3-3.1.1.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/commons-net-3.1.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/curator-client-2.7.1.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/curator-framework-2.7.1.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/curator-recipes-2.7.1.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/guava-11.0.2.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/guice-3.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-annotations-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-ant-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-archive-logs-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-archives-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-auth-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-aws-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-azure-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-azure-datalake-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-common-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-datajoin-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-distcp-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-extras-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-gridmix-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-hdfs-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-hdfs-nfs-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-mapreduce-client-app-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-mapreduce-client-common-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-mapreduce-client-core-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-mapreduce-client-hs-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-mapreduce-client-hs-plugins-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-mapreduce-client-jobclient-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-mapreduce-client-nativetask-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-mapreduce-client-shuffle-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-mapreduce-examples-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-nfs-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-openstack-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-rumen-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-sls-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-streaming-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-yarn-api-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-yarn-applications-distributedshell-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-yarn-applications-unmanaged-am-launcher-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-yarn-client-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-yarn-common-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-yarn-registry-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-yarn-server-applicationhistoryservice-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-yarn-server-common-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-yarn-server-nodemanager-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-yarn-server-resourcemanager-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-yarn-server-web-proxy-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hamcrest-core-1.3.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/htrace-core4-4.0.1-incubating.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/httpclient-4.2.5.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/httpcore-4.2.5.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hue-plugins-3.9.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/jackson-annotations-2.2.3.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/jackson-core-2.2.3.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/jackson-core-asl-1.8.8.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/jackson-databind-2.2.3.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/jackson-mapper-asl-1.8.8.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/jasper-compiler-5.5.23.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/jasper-runtime-5.5.23.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/java-xmlbuilder-0.4.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/javax.inject-1.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/jaxb-api-2.2.2.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/jaxb-impl-2.2.3-1.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/jets3t-0.9.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/jettison-1.1.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/jline-2.11.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/jsch-0.1.42.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/jsr305-3.0.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/leveldbjni-all-1.8.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/log4j-1.2.17.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/metrics-core-3.0.2.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/microsoft-windowsazure-storage-sdk-0.6.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/mockito-all-1.8.5.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/netty-3.10.5.Final.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/okhttp-2.4.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/okio-1.4.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/paranamer-2.3.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/protobuf-java-2.5.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/slf4j-api-1.7.5.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/slf4j-log4j12-1.7.5.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/snappy-java-1.0.4.1.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/spark-1.6.0-cdh5.13.0-yarn-shuffle.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/stax-api-1.0-2.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/xercesImpl-2.9.1.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/xml-apis-1.3.04.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/xmlenc-0.52.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/zookeeper-3.4.5-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/lib/hadoop/LICENSE.txt:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/lib/hadoop/NOTICE.txt:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/lib/hadoop/lib/jsp-api-2.1.jar:/home1/opt/cloudera/parcels/GPLEXTRAS-5.13.0-1.cdh5.13.0.p0.29/lib/hadoop/lib/COPYING.hadoop-lzo:/home1/opt/cloudera/parcels/GPLEXTRAS-5.13.0-1.cdh5.13.0.p0.29/lib/hadoop/lib/hadoop-lzo-0.4.15-cdh5.13.0.jar
 SPARK_EXTRA_LIB_PATH:null
 LD_LIBRARY_PATH::/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/lib/hadoop/lib/native:/home1/opt/cloudera/parcels/GPLEXTRAS-5.13.0-1.cdh5.13.0.p0.29/lib/hadoop/lib/native
 Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
 19/01/10 22:30:26 WARN SparkConf: The configuration key 'spark.yarn.executor.memoryOverhead' has been deprecated as of Spark 2.3 and may be removed in the future. Please use the new key 'spark.executor.memoryOverhead' instead.
 19/01/10 22:30:27 INFO TestSubmit: 提交任务，任务名称：test yarn submit app
 19/01/10 22:30:27 INFO RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
 19/01/10 22:30:29 INFO Client: Requesting a new application from cluster with 6 NodeManagers
 19/01/10 22:30:29 INFO Client: Verifying our application has not requested more than the maximum memory capability of the cluster (30282 MB per container)
 19/01/10 22:30:29 INFO Client: Will allocate AM container, with 3456 MB memory including 384 MB overhead
 19/01/10 22:30:29 INFO Client: Setting up container launch context for our AM
 19/01/10 22:30:29 INFO Client: Setting up the launch environment for our AM container
 19/01/10 22:30:29 INFO Client: Preparing resources for our AM container
 19/01/10 22:30:34 WARN Client: Neither spark.yarn.jars nor spark.yarn.archive is set, falling back to uploading libraries under SPARK_HOME.
 19/01/10 22:30:36 INFO Client: Uploading resource file:/tmp/spark-03699598-b859-4a74-a65f-bc63e9fae733/__spark_libs__4116956896087694051.zip -> hdfs://vm192.168.0.141.com.cn:8020/user/.sparkStaging/application_1543820999543_0236/__spark_libs__4116956896087694051.zip
 19/01/10 22:30:43 INFO Client: Uploading resource file:/home1/zhangsan/mrs-streaming-driver.jar -> hdfs://vm192.168.0.141.com.cn:8020/user/.sparkStaging/application_1543820999543_0236/mrs-streaming-driver.jar
 19/01/10 22:31:33 INFO Client: Uploading resource file:/home1/zhangsan/sparkjars/elasticsearch-cli-6.4.2.jar -> hdfs://vm192.168.0.141.com.cn:8020/user/.sparkStaging/application_1543820999543_0236/elasticsearch-cli-6.4.2.jar
 19/01/10 22:31:33 INFO Client: Uploading resource file:/home1/zhangsan/sparkjars/elasticsearch-6.4.2.jar -> hdfs://vm192.168.0.141.com.cn:8020/user/.sparkStaging/application_1543820999543_0236/elasticsearch-6.4.2.jar
 ......
 19/01/10 22:31:33 INFO Client: Uploading resource file:/tmp/spark-03699598-b859-4a74-a65f-bc63e9fae733/__spark_conf__339930271770719398.zip -> hdfs://vm192.168.0.141.com.cn:8020/user/.sparkStaging/application_1543820999543_0236/__spark_conf__.zip
 19/01/10 22:31:34 INFO SecurityManager: Changing view acls to: zhangsan
 19/01/10 22:31:34 INFO SecurityManager: Changing modify acls to: zhangsan
 19/01/10 22:31:34 INFO SecurityManager: Changing view acls groups to:
 19/01/10 22:31:34 INFO SecurityManager: Changing modify acls groups to:
 19/01/10 22:31:34 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users  with view permissions: Set(zhangsan); groups with view permissions: Set(); users  with modify permissions: Set(zhangsan); groups with modify permissions: Set()
 19/01/10 22:31:34 INFO Client: Submitting application application_1543820999543_0236 to ResourceManager
 19/01/10 22:31:34 INFO YarnClientImpl: Submitted application application_1543820999543_0236
 application id is application_1543820999543_0236
 Complete ....
 19/01/10 22:31:34 INFO ShutdownHookManager: Shutdown hook called
 19/01/10 22:31:34 INFO ShutdownHookManager: Deleting directory /tmp/spark-03699598-b859-4a74-a65f-bc63e9fae733
 bash-4.1$ 

目前调试通之后，测试通过yarn的cluster方式，client模式下任务提交到yarn上去无响应。

spark任务状态：

任务状态封装类

 public class SparkTaskState{
     private String appId;
     private String state;
     private float progress;
     private String finalStatus;

     public String getAppId() {
         return appId;
     }
     public void setAppId(String appId) {
         this.appId = appId;
     }

     public String getState() {
         return state;
     }
     public void setState(String state) {
         this.state = state;
     }

     public float getProgress() {
         return progress;
     }
     public void setProgress(float progress) {
         this.progress = progress;
     }

     public String getFinalStatus() {
         return finalStatus;
     }
     public void setFinalStatus(String finalStatus) {
         this.finalStatus = finalStatus;
     }
 }

    /**
     * 获取spark任务状态
     *
     * @param yarnResourcemanagerAddress
     *            yarn资源管理器地址， 例如：master:8032，查看yarn集群获取具体地址
     * @param appIdStr
     *            需要取消的任务id
     */
    public static SparkTaskState getStatus(String yarnResourcemanagerAddress, String appIdStr) {
        logger.info("获取任务状态启动，任务id：" + appIdStr);

        // 初始化 yarn的配置
        Configuration cf = new Configuration();

        boolean cross_platform = false;
        String os = System.getProperty("os.name");
        if (os.contains("Windows")) {
            cross_platform = true;
        }
        cf.setBoolean("mapreduce.app-submission.cross-platform", cross_platform);// 配置使用跨平台提交任务

        // 设置yarn资源，不然会使用localhost:8032
        cf.set("yarn.resourcemanager.address", yarnResourcemanagerAddress);

        logger.info("获取任务状态，任务id:" + appIdStr);
        SparkTaskState taskState = new SparkTaskState();

        // 设置任务id
        taskState.setAppId(appIdStr);

        YarnClient yarnClient = YarnClient.createYarnClient();
        // 初始化yarn的客户端
        yarnClient.init(cf);
        // yarn客户端启动
        yarnClient.start();

        ApplicationReport report = null;
        try {
            report = yarnClient.getApplicationReport(getAppId(appIdStr));
        } catch (Exception e) {
            logger.error("获取spark任务状态失败");
        }

        if (report != null) {
            YarnApplicationState state = report.getYarnApplicationState();
            taskState.setState(state.name());

            // 任务执行进度
            float progress = report.getProgress();
            taskState.setProgress(progress);

            // 最终状态
            FinalApplicationStatus status = report.getFinalApplicationStatus();
            taskState.setFinalStatus(status.name());
        } else {
            taskState.setState("failed");
            taskState.setProgress(0.0f);
            taskState.setFinalStatus("failed");
        }

        // 关闭yarn客户端
        yarnClient.stop();

        logger.info("获取任务状态结束，任务状态：" + JSON.toJSONString(taskState));

        return taskState;
    }

    private static ApplicationId getAppId(String appIdStr) {
        return ConverterUtils.toApplicationId(appIdStr);
    }

spark日志跟踪：

请参考《https://www.cnblogs.com/lyy-blog/p/9635601.html》

spark关闭任务：

    /**
     * 停止spark任务
     *
     * @param yarnResourcemanagerAddress
     *            yarn资源管理器地址， 例如：master:8032，查看yarn集群获取具体地址
     * @param appIdStr
     *            需要取消的任务id
     */
    public static void killJob(String yarnResourcemanagerAddress, String appIdStr) {
        logger.info("取消spark任务,任务id：" + appIdStr);

        // 初始化 yarn的配置
        Configuration cf = new Configuration();

        boolean cross_platform = false;
        String os = System.getProperty("os.name");
        if (os.contains("Windows")) {
            cross_platform = true;
        }
        // 配置使用跨平台提交任务
        cf.setBoolean("mapreduce.app-submission.cross-platform", cross_platform);
        // 设置yarn资源，不然会使用localhost:8032
        cf.set("yarn.resourcemanager.address", yarnResourcemanagerAddress);

        // 创建yarn的客户端，此类中有杀死任务的方法
        YarnClient yarnClient = YarnClient.createYarnClient();

        // 初始化yarn的客户端
        yarnClient.init(cf);

        // yarn客户端启动
        yarnClient.start();

        try {
            // 根据应用id，杀死应用
            yarnClient.killApplication(getAppId(appIdStr));
        } catch (Exception e) {
            logger.error("取消spark任务失败", e);
        }

        // 关闭yarn客户端
        yarnClient.stop();
    }

参考文章：https://blog.csdn.net/weixin_36647532/article/details/80766350