Apache Spark YARN Integration

import org.apache.spark.deploy.yarn.{Client, ClientArguments, ApplicationMaster, ApplicationMasterArguments}
import org.apache.spark.deploy.yarn.{YarnRMClient, YarnAllocator, AllocationType}
import org.apache.spark.scheduler.cluster.{YarnClientSchedulerBackend, YarnClusterSchedulerBackend}
import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.yarn.api.records._

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

// Create Spark configuration
val sparkConf = new SparkConf()
  .setAppName("My Spark App")
  .set("spark.executor.memory", "2g")
  .set("spark.executor.cores", "2")

// Configure YARN client
val hadoopConf = new Configuration()
val args = Array("--jar", "my-app.jar", "--class", "MyMainClass")
val clientArgs = new ClientArguments(args, sparkConf)

// Submit application to YARN
val client = new Client(clientArgs, hadoopConf, sparkConf)
// Application submission handled by Spark runtime

class Client(
  args: ClientArguments, 
  hadoopConf: Configuration, 
  sparkConf: SparkConf
) extends ClientBase {
  def stop(): Unit
}

private[spark] class ClientArguments(args: Array[String], sparkConf: SparkConf) {
  var addJars: String
  var files: String
  var archives: String
  var userJar: String
  var userClass: String
  var userArgs: Seq[String]
  var executorMemory: Int
  var executorCores: Int
  var numExecutors: Int
  var amQueue: String
  var amMemory: Int
  var appName: String
  var priority: Int
  val amMemoryOverhead: Int
  val executorMemoryOverhead: Int
}

class ApplicationMaster(
  args: ApplicationMasterArguments, 
  client: YarnRMClient
) {
  // Application lifecycle management
  // Resource negotiation with YARN ResourceManager  
  // Executor management and monitoring
}

class ApplicationMasterArguments(val args: Array[String]) {
  var userJar: String
  var userClass: String
  var userArgs: Seq[String]
  var executorMemory: Int
  var executorCores: Int
  var numExecutors: Int
  def printUsageAndExit(exitCode: Int, unknownParam: Any = null): Unit
}

class YarnClientSchedulerBackend(
  scheduler: TaskSchedulerImpl, 
  sc: SparkContext
) extends YarnSchedulerBackend {
  def start(): Unit
}

class YarnClusterSchedulerBackend extends YarnSchedulerBackend

class YarnClientClusterScheduler(sc: SparkContext) extends TaskSchedulerImpl

class YarnClusterScheduler(sc: SparkContext) extends TaskSchedulerImpl

trait YarnRMClient {
  // ResourceManager client interface
}

abstract class YarnAllocator {
  // Abstract base class for YARN resource allocation logic
}

object AllocationType extends Enumeration {
  // Enumeration for YARN allocation types
}

class YarnSparkHadoopUtil extends SparkHadoopUtil {
  // YARN-specific Hadoop utilities
}

class ClientDistributedCacheManager {
  // Manages distributed cache for YARN applications  
}

trait ExecutorRunnableUtil {
  // Utility trait for executor container management
}

// Core argument and configuration types
private[spark] class ClientArguments(args: Array[String], sparkConf: SparkConf)
class ApplicationMasterArguments(val args: Array[String])

// Resource management interfaces
trait YarnRMClient
class YarnRMClientImpl(args: ApplicationMasterArguments) extends YarnRMClient
private[yarn] abstract class YarnAllocator(
  conf: Configuration,
  sparkConf: SparkConf,
  appAttemptId: ApplicationAttemptId,
  args: ApplicationMasterArguments,
  preferredNodes: collection.Map[String, collection.Set[SplitInfo]],
  securityMgr: SecurityManager
) extends Logging

// Allocation strategy enumeration
object AllocationType extends Enumeration {
  type AllocationType = Value
  val HOST, RACK, ANY = Value
}

// Scheduler and backend types  
private[spark] abstract class YarnSchedulerBackend extends CoarseGrainedSchedulerBackend
private[spark] class TaskSchedulerImpl extends TaskScheduler

// Client and utility traits
private[spark] trait ClientBase
trait ExecutorRunnableUtil