Scheduler Integration

/**
 * Cluster manager implementation for YARN integration
 * Implements ExternalClusterManager interface
 */
private[spark] class YarnClusterManager extends ExternalClusterManager {
  /**
   * Check if this manager can create components for the given master URL
   * @param masterURL Master URL (should be "yarn")
   * @return true if masterURL is "yarn"
   */
  def canCreate(masterURL: String): Boolean
  
  /**
   * Create YARN task scheduler
   * @param sc SparkContext
   * @param masterURL Master URL
   * @return TaskScheduler instance
   */
  def createTaskScheduler(sc: SparkContext, masterURL: String): TaskScheduler
  
  /**
   * Create YARN scheduler backend
   * @param sc SparkContext
   * @param masterURL Master URL  
   * @param scheduler TaskScheduler instance
   * @return SchedulerBackend instance
   */
  def createSchedulerBackend(
    sc: SparkContext,
    masterURL: String,
    scheduler: TaskScheduler
  ): SchedulerBackend
  
  /**
   * Initialize scheduler components
   * @param scheduler TaskScheduler to initialize
   * @param backend SchedulerBackend to initialize
   */
  def initialize(scheduler: TaskScheduler, backend: SchedulerBackend): Unit
}

import org.apache.spark.{SparkConf, SparkContext}

// YarnClusterManager is automatically used when master is "yarn"
val conf = new SparkConf()
  .setMaster("yarn")
  .setAppName("YarnIntegrationExample")

val sc = new SparkContext(conf)
// YarnClusterManager creates appropriate scheduler and backend automatically

/**
 * Abstract base class for YARN scheduler backends
 * @param scheduler TaskScheduler implementation
 * @param sc SparkContext
 */
private[spark] abstract class YarnSchedulerBackend(
  scheduler: TaskSchedulerImpl,
  sc: SparkContext
) extends CoarseGrainedSchedulerBackend(scheduler, sc.env.rpcEnv) {
  
  /**
   * Bind scheduler backend to YARN application
   * @param appId YARN application ID
   * @param attemptId Optional application attempt ID
   */
  protected def bindToYarn(appId: ApplicationId, attemptId: Option[ApplicationAttemptId]): Unit
  
  /** Get YARN application ID as string */
  def applicationId(): String
  
  /** Get application attempt ID as string */
  def applicationAttemptId(): Option[String]
  
  /** Request total executors from YARN */
  def doRequestTotalExecutors(): Future[Boolean]
  
  /**
   * Kill specific executors
   * @param executorIds Sequence of executor IDs to kill
   * @return Future indicating success/failure
   */
  def doKillExecutors(executorIds: Seq[String]): Future[Boolean]
  
  /** Get shuffle service merger locations */
  def getShufflePushMergerLocations(): Seq[BlockManagerId]
}

/**
 * Scheduler backend for yarn-client mode
 * @param scheduler TaskScheduler implementation
 * @param sc SparkContext
 */
private[spark] class YarnClientSchedulerBackend(
  scheduler: TaskSchedulerImpl,
  sc: SparkContext
) extends YarnSchedulerBackend(scheduler, sc) {
  
  /** Start client mode scheduler backend */
  def start(): Unit
  
  /**
   * Stop scheduler backend
   * @param exitCode Exit code for cleanup
   */
  def stop(exitCode: Int): Unit
}

import org.apache.spark.{SparkConf, SparkContext}

// Client mode - driver runs locally, executors on YARN
val conf = new SparkConf()
  .setMaster("yarn")
  .setDeployMode("client")  // or spark.submit.deployMode=client
  .setAppName("ClientModeApp")

val sc = new SparkContext(conf)  // Uses YarnClientSchedulerBackend automatically

/**
 * Scheduler backend for yarn-cluster mode
 * @param scheduler TaskScheduler implementation
 * @param sc SparkContext
 */
private[spark] class YarnClusterSchedulerBackend(
  scheduler: TaskSchedulerImpl,
  sc: SparkContext
) extends YarnSchedulerBackend(scheduler, sc) {
  
  /** Start cluster mode scheduler backend */
  def start(): Unit
  
  /**
   * Stop scheduler backend
   * @param exitCode Exit code for cleanup
   */
  def stop(exitCode: Int): Unit
  
  /** Get driver container log URLs */
  def getDriverLogUrls(): Option[Map[String, String]]
  
  /** Get driver container attributes */
  def getDriverAttributes(): Option[Map[String, String]]
}

import org.apache.spark.{SparkConf, SparkContext}

// Cluster mode - both driver and executors on YARN
val conf = new SparkConf()
  .setMaster("yarn")
  .setDeployMode("cluster")  // or spark.submit.deployMode=cluster
  .setAppName("ClusterModeApp")

val sc = new SparkContext(conf)  // Uses YarnClusterSchedulerBackend automatically

/**
 * Task scheduler with YARN-specific enhancements
 * @param sc SparkContext
 */
class YarnScheduler(sc: SparkContext) extends TaskSchedulerImpl {
  
  /**
   * Get rack information for hosts
   * @param hostPorts Sequence of host:port strings
   * @return Sequence of optional rack names
   */
  def getRacksForHosts(hostPorts: Seq[String]): Seq[Option[String]]
}

/**
 * Task scheduler for yarn-cluster mode
 * @param sc SparkContext
 */
class YarnClusterScheduler(sc: SparkContext) extends YarnScheduler {
  
  /** Post-start initialization hook */
  def postStartHook(): Unit
}

import org.apache.spark.{SparkConf, SparkContext}

// Client mode (default)
val clientConf = new SparkConf()
  .setMaster("yarn")
  .setAppName("AutoClientMode")
// Creates: YarnScheduler + YarnClientSchedulerBackend

// Cluster mode
val clusterConf = new SparkConf()
  .setMaster("yarn")
  .setDeployMode("cluster")
  .setAppName("AutoClusterMode")
// Creates: YarnClusterScheduler + YarnClusterSchedulerBackend

import org.apache.spark.SparkConf

val conf = new SparkConf()
  .setMaster("yarn")
  .setAppName("CustomSchedulerConfig")
  // Scheduler-specific configurations
  .set("spark.scheduler.mode", "FAIR")
  .set("spark.scheduler.allocation.file", "/path/to/fairscheduler.xml")
  .set("spark.scheduler.maxRegisteredResourcesWaitingTime", "30s")
  .set("spark.scheduler.minRegisteredResourcesRatio", "0.8")

// Configure executor resources
val conf = new SparkConf()
  .setMaster("yarn")
  .set("spark.executor.instances", "10")
  .set("spark.executor.cores", "2")
  .set("spark.executor.memory", "4g")
  .set("spark.executor.memoryOverhead", "512m")
  // Dynamic allocation
  .set("spark.dynamicAllocation.enabled", "true")
  .set("spark.dynamicAllocation.minExecutors", "2")
  .set("spark.dynamicAllocation.maxExecutors", "20")
  .set("spark.dynamicAllocation.initialExecutors", "5")

import org.apache.spark.scheduler.cluster.YarnScheduler

// YarnScheduler automatically provides rack awareness
val scheduler = new YarnScheduler(sparkContext)

// Get rack information for data locality optimization
val hosts = Seq("worker1:7337", "worker2:7337", "worker3:7337")
val racks = scheduler.getRacksForHosts(hosts)
// Returns rack information if available from YARN

// Configure locality preferences
val conf = new SparkConf()
  .set("spark.locality.wait", "3s")
  .set("spark.locality.wait.node", "0")
  .set("spark.locality.wait.rack", "0")

// The lifecycle is typically managed automatically, but understanding the flow:

// 1. YarnClusterManager.createSchedulerBackend() creates appropriate backend
// 2. Backend.start() initializes communication with YARN
// 3. Backend.bindToYarn() connects to YARN application
// 4. Backend handles executor requests via doRequestTotalExecutors()
// 5. Backend.stop() cleans up resources

// For custom integration:
class CustomYarnSchedulerBackend(scheduler: TaskSchedulerImpl, sc: SparkContext)
  extends YarnSchedulerBackend(scheduler, sc) {
  
  override def start(): Unit = {
    super.start()
    // Custom initialization logic
  }
  
  override def doRequestTotalExecutors(): Future[Boolean] = {
    // Custom executor request logic
    super.doRequestTotalExecutors()
  }
}

// Monitor scheduler backend status
val backend = sparkContext.schedulerBackend.asInstanceOf[YarnSchedulerBackend]

// Get YARN application information
val appId = backend.applicationId()
val attemptId = backend.applicationAttemptId()

println(s"Application ID: $appId")
println(s"Attempt ID: $attemptId")

// For cluster mode, get driver information
backend match {
  case clusterBackend: YarnClusterSchedulerBackend =>
    val driverLogs = clusterBackend.getDriverLogUrls()
    val driverAttrs = clusterBackend.getDriverAttributes()
    println(s"Driver logs: $driverLogs")
    println(s"Driver attributes: $driverAttrs")
  case _ => // Client mode
}

import scala.concurrent.Future
import scala.concurrent.ExecutionContext.Implicits.global

// Request additional executors
val backend = sparkContext.schedulerBackend.asInstanceOf[YarnSchedulerBackend]
val requestFuture: Future[Boolean] = backend.doRequestTotalExecutors()

requestFuture.onComplete {
  case Success(true) => println("Executor request successful")
  case Success(false) => println("Executor request failed")
  case Failure(exception) => println(s"Request failed with exception: $exception")
}

// Kill specific executors
val executorsToKill = Seq("executor-1", "executor-2")
val killFuture: Future[Boolean] = backend.doKillExecutors(executorsToKill)