YARN Client Management

/**
 * YARN client implementation for stable API (Hadoop 2.2+)
 * Handles application submission, monitoring, and resource management
 */
class Client(
  args: ClientArguments, 
  hadoopConf: Configuration, 
  sparkConf: SparkConf
) extends ClientBase {
  /**
   * Stop the YARN client and clean up resources
   */
  def stop(): Unit
}

/**
 * Alternative constructors for simplified client creation
 */
object Client {
  def apply(clientArgs: ClientArguments, spConf: SparkConf): Client
  def apply(clientArgs: ClientArguments): Client
}

import org.apache.spark.deploy.yarn.{Client, ClientArguments}
import org.apache.spark.SparkConf
import org.apache.hadoop.conf.Configuration

// Full constructor with explicit Hadoop configuration
val sparkConf = new SparkConf().setAppName("MyApp")
val hadoopConf = new Configuration()
val args = Array("--jar", "myapp.jar", "--class", "MyMainClass")
val clientArgs = new ClientArguments(args, sparkConf)
val client = new Client(clientArgs, hadoopConf, sparkConf)

// Simplified constructor
val client2 = new Client(clientArgs, sparkConf)

// Stop client when done
client.stop()

/**
 * Base trait for YARN client functionality
 * Provides core application submission logic and resource management
 */
private[spark] trait ClientBase {
  // Application submission and monitoring capabilities
  // Resource allocation and management  
  // YARN application lifecycle management
}

/**
 * Companion object with shared client utilities
 */
private[spark] object ClientBase {
  // Shared client utility methods and constants
}

/**
 * Client configuration and argument parsing for YARN operations
 * Parses command-line arguments and manages application submission parameters
 */
private[spark] class ClientArguments(args: Array[String], sparkConf: SparkConf) {
  /** Additional JARs to distribute with the application */
  var addJars: String = null
  
  /** Files to distribute to executor working directories */
  var files: String = null
  
  /** Archives to distribute and extract on executors */  
  var archives: String = null
  
  /** User application JAR file */
  var userJar: String = null
  
  /** User application main class */
  var userClass: String = null
  
  /** Arguments to pass to user application */
  var userArgs: Seq[String] = Seq[String]()
  
  /** Executor memory in MB (default: 1024) */
  var executorMemory: Int = 1024
  
  /** Number of cores per executor (default: 1) */
  var executorCores: Int = 1
  
  /** Total number of executors to request */
  var numExecutors: Int = DEFAULT_NUMBER_EXECUTORS
  
  /** YARN queue name (default: "default") */
  var amQueue: String = sparkConf.get("spark.yarn.queue", "default")
  
  /** ApplicationMaster memory in MB (default: 512) */
  var amMemory: Int = 512
  
  /** Application name (default: "Spark") */
  var appName: String = "Spark"
  
  /** Application priority (default: 0) */
  var priority: Int = 0
  
  /** Additional memory overhead for ApplicationMaster container */
  val amMemoryOverhead: Int = sparkConf.getInt("spark.yarn.driver.memoryOverhead", 
    math.max((MEMORY_OVERHEAD_FACTOR * amMemory).toInt, MEMORY_OVERHEAD_MIN))
  
  /** Additional memory overhead for executor containers */
  val executorMemoryOverhead: Int = sparkConf.getInt("spark.yarn.executor.memoryOverhead",
    math.max((MEMORY_OVERHEAD_FACTOR * executorMemory).toInt, MEMORY_OVERHEAD_MIN))
}

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

// Basic argument configuration
val sparkConf = new SparkConf()
val args = Array(
  "--jar", "/path/to/myapp.jar",
  "--class", "com.example.MyMainClass",
  "--arg", "appArg1",
  "--arg", "appArg2",
  "--executor-memory", "2g",
  "--executor-cores", "2",
  "--num-executors", "4"
)

val clientArgs = new ClientArguments(args, sparkConf)

// Access parsed arguments
println(s"User JAR: ${clientArgs.userJar}")
println(s"Main class: ${clientArgs.userClass}")
println(s"Executor memory: ${clientArgs.executorMemory} MB")
println(s"Number of executors: ${clientArgs.numExecutors}")

/**
 * Main entry point for YARN client operations
 * Typically invoked by spark-submit in YARN mode
 */
object Client {
  def main(args: Array[String]): Unit
}

// Configuration integration example
val sparkConf = new SparkConf()
  .setAppName("MySparkApp")
  .set("spark.executor.memory", "2g")
  .set("spark.yarn.queue", "production")

val hadoopConf = new Configuration()
hadoopConf.set("yarn.nodemanager.aux-services", "mapreduce_shuffle")

val client = new Client(clientArgs, hadoopConf, sparkConf)