Application Management

/**
 * Client for submitting and monitoring Spark applications on YARN
 * @param args Client arguments containing application details
 * @param sparkConf Spark configuration
 * @param rpcEnv RPC environment for communication
 */
private[spark] class Client(
  val args: ClientArguments,
  val sparkConf: SparkConf,
  val rpcEnv: RpcEnv
) {
  /** Submit application to YARN ResourceManager */
  def submitApplication(): Unit
  
  /** Submit and monitor application until completion */
  def run(): Unit
  
  /** Stop the client and cleanup resources */
  def stop(): Unit
  
  /** Get YARN application ID */
  def getApplicationId(): ApplicationId
  
  /** Monitor application status and return report */
  def monitorApplication(
    returnOnRunning: Boolean = false,
    logApplicationReport: Boolean = true,
    interval: Long = sparkConf.get(REPORT_INTERVAL)
  ): YarnAppReport
  
  /** Report launcher state for external monitoring */
  def reportLauncherState(state: SparkAppHandle.State): Unit
  
  /** Get application report from ResourceManager */
  def getApplicationReport(): ApplicationReport
}

import org.apache.spark.deploy.yarn.{Client, ClientArguments}
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.rpc.RpcEnv

val sparkConf = new SparkConf()
  .setAppName("MySparkApp")
  .set("spark.yarn.queue", "default")

val rpcEnv = RpcEnv.create("sparkYarnClient", "localhost", -1, sparkConf, 
  new SecurityManager(sparkConf))

val clientArgs = new ClientArguments(Array(
  "--jar", "/path/to/app.jar",
  "--class", "com.example.MainClass"
))

val client = new Client(clientArgs, sparkConf, rpcEnv)
client.submitApplication()
val appId = client.getApplicationId()
println(s"Application submitted with ID: $appId")

object Client {
  /** Application JAR file name in YARN */
  val APP_JAR_NAME: String = "__app__.jar"
  
  /** Staging directory name */
  val SPARK_STAGING: String = ".sparkStaging"
  
  /** Localized configuration directory name */
  val LOCALIZED_CONF_DIR: String = "__spark_conf__"
  
  /** Localized library directory name */
  val LOCALIZED_LIB_DIR: String = "__spark_libs__"
  
  /** Localized Python files directory name */
  val LOCALIZED_PYTHON_DIR: String = "__pyfiles__"
  
  /**
   * Get user classpath URIs from Spark configuration
   * @param conf Spark configuration
   * @return Array of classpath URIs
   */
  def getUserClasspath(conf: SparkConf): Array[URI]
  
  /**
   * Get user classpath URLs
   * @param conf Spark configuration
   * @param useClusterPath Whether to use cluster-side paths
   * @return Array of classpath URLs
   */
  def getUserClasspathUrls(conf: SparkConf, useClusterPath: Boolean): Array[URL]
  
  /**
   * Build file system path from components
   * @param components Path components to join
   * @return Combined path string
   */
  def buildPath(components: String*): String
  
  /**
   * Convert gateway path to cluster path
   * @param conf Spark configuration
   * @param path Original path
   * @return Cluster-side path
   */
  def getClusterPath(conf: SparkConf, path: String): String
}

/**
 * YARN ApplicationMaster for managing Spark application lifecycle
 * @param args ApplicationMaster arguments
 * @param sparkConf Spark configuration
 * @param yarnConf YARN configuration
 */
private[spark] class ApplicationMaster(
  args: ApplicationMasterArguments,
  sparkConf: SparkConf,
  yarnConf: YarnConfiguration
) {
  /** Main execution method for ApplicationMaster */
  def run(): Int
  
  /** Run in unmanaged mode (client mode) */
  def runUnmanaged(
    clientRpcEnv: RpcEnv,
    appAttemptId: ApplicationAttemptId,
    stagingDir: Path,
    cachedResourcesConf: SparkConf
  ): Unit
  
  /** Stop unmanaged ApplicationMaster */
  def stopUnmanaged(stagingDir: Path): Unit
  
  /** Finish application with final status */
  def finish(status: FinalApplicationStatus, code: Int, msg: String): Unit
  
  /** Unregister from ResourceManager */
  def unregister(status: FinalApplicationStatus, diagnostics: String): Unit
}

object ApplicationMaster {
  /** Main entry point for ApplicationMaster */
  def main(args: Array[String]): Unit
  
  /** Signal SparkContext initialization */
  def sparkContextInitialized(sc: SparkContext): Unit
  
  /** Get current application attempt ID */
  def getAttemptId(): ApplicationAttemptId
  
  /** Get Spark history server address */
  def getHistoryServerAddress(
    sparkConf: SparkConf,
    yarnConf: YarnConfiguration,
    appId: String,
    attemptId: String
  ): String
}

/**
 * Application entry point for yarn-cluster mode
 */
class YarnClusterApplication extends SparkApplication {
  /**
   * Start application in cluster mode
   * @param args Application arguments
   * @param conf Spark configuration
   */
  def start(args: Array[String], conf: SparkConf): Unit
}

import org.apache.spark.deploy.yarn.YarnClusterApplication
import org.apache.spark.SparkConf

val conf = new SparkConf()
  .setAppName("MyClusterApp")
  .set("spark.yarn.queue", "default")

val app = new YarnClusterApplication()
app.start(Array("arg1", "arg2"), conf)

object ExecutorLauncher {
  /** Main entry point for executor launcher */
  def main(args: Array[String]): Unit
}

/**
 * Container for YARN application status information
 * @param appState Current YARN application state
 * @param finalState Final application status
 * @param diagnostics Optional diagnostic messages
 */
case class YarnAppReport(
  appState: YarnApplicationState,
  finalState: FinalApplicationStatus,
  diagnostics: Option[String]
)

/**
 * Argument parser for YARN client
 * @param args Command line arguments
 */
class ClientArguments(args: Array[String]) {
  /** User application JAR file */
  var userJar: String
  
  /** Main class to execute */
  var userClass: String
  
  /** Primary Python file for PySpark */
  var primaryPyFile: String
  
  /** Primary R file for SparkR */
  var primaryRFile: String
  
  /** User application arguments */
  var userArgs: ArrayBuffer[String]
  
  /** Enable verbose logging */
  var verbose: Boolean
}

/**
 * Argument parser for ApplicationMaster
 * @param args Command line arguments
 */
class ApplicationMasterArguments(args: Array[String]) {
  /** User application JAR file path */
  var userJar: String
  
  /** Main class name to execute */
  var userClass: String
  
  /** Primary Python file for PySpark */
  var primaryPyFile: String
  
  /** Primary R file for SparkR */
  var primaryRFile: String
  
  /** User application arguments */
  var userArgs: Seq[String]
  
  /** Spark properties file path */
  var propertiesFile: String
  
  /** Distributed cache configuration */
  var distCacheConf: String
}

// Client mode configuration
val conf = new SparkConf()
  .setMaster("yarn")
  .setDeployMode("client")
  .setAppName("ClientModeApp")

// SparkContext creation automatically uses YARN client mode
val sc = new SparkContext(conf)

// Cluster mode configuration
val conf = new SparkConf()
  .setMaster("yarn")
  .setDeployMode("cluster")
  .setAppName("ClusterModeApp")

// For cluster mode, typically submitted via spark-submit
// or programmatically via YarnClusterApplication

import org.apache.spark.deploy.yarn.{Client, ClientArguments}
import org.apache.spark.SparkConf
import org.apache.spark.rpc.RpcEnv

def submitApplication(jarPath: String, mainClass: String): ApplicationId = {
  val conf = new SparkConf()
    .setAppName("ProgrammaticSubmission")
    .set("spark.yarn.queue", "default")
    .set("spark.yarn.am.memory", "1g")

  val rpcEnv = RpcEnv.create("sparkYarnClient", "localhost", -1, conf,
    new SecurityManager(conf))

  val args = new ClientArguments(Array(
    "--jar", jarPath,
    "--class", mainClass
  ))

  val client = new Client(args, conf, rpcEnv)
  client.submitApplication()
  client.getApplicationId()
}