Scheduler Backends

/**
 * Scheduler backend for YARN client mode
 * Manages executor lifecycle and resource requests through YARN ApplicationMaster
 */
private[spark] class YarnClientSchedulerBackend(
  scheduler: TaskSchedulerImpl, 
  sc: SparkContext
) extends YarnSchedulerBackend {
  
  /**
   * Start the backend and submit application to YARN
   * Initiates ApplicationMaster and begins executor allocation
   */
  def start(): Unit
  
  // Application monitoring and lifecycle management
  // Resource request handling through ApplicationMaster
  // Executor status tracking and failure handling
}

import org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend
import org.apache.spark.scheduler.TaskSchedulerImpl
import org.apache.spark.SparkContext

// Typically created by Spark runtime in client mode
val sc = new SparkContext(sparkConf)
val taskScheduler = new TaskSchedulerImpl(sc)
val backend = new YarnClientSchedulerBackend(taskScheduler, sc)

// Backend lifecycle managed by Spark runtime
backend.start()

/**
 * Scheduler backend for YARN cluster mode  
 * Manages executors when driver runs within ApplicationMaster
 */
private[spark] class YarnClusterSchedulerBackend extends YarnSchedulerBackend {
  // Cluster-specific resource management
  // Direct integration with ApplicationMaster
  // Optimized executor allocation for cluster deployment
}

import org.apache.spark.scheduler.cluster.YarnClusterSchedulerBackend

// Created by Spark runtime in cluster mode
// Driver runs inside ApplicationMaster on YARN cluster
val backend = new YarnClusterSchedulerBackend()

/**
 * Base class for YARN scheduler backend implementations
 * Provides common functionality for both client and cluster modes
 */
private[spark] abstract class YarnSchedulerBackend 
    extends CoarseGrainedSchedulerBackend {
  
  // Common YARN resource management operations
  // Executor container lifecycle management  
  // Resource request and allocation handling
  // Integration with YARN ApplicationMaster
}

/**
 * Task scheduler for YARN client mode
 * Optimized for client-side driver with remote executors
 */
private[spark] class YarnClientClusterScheduler(sc: SparkContext) 
    extends TaskSchedulerImpl {
  
  // Client-specific scheduling optimizations
  // Remote executor communication handling
  // Task distribution strategies for client mode
}

/**
 * Task scheduler for YARN cluster mode
 * Optimized for driver and executors running on same cluster  
 */
private[spark] class YarnClusterScheduler(sc: SparkContext) 
    extends TaskSchedulerImpl {
  
  // Cluster-specific scheduling optimizations
  // Locality-aware task placement
  // Efficient intra-cluster communication
}

import org.apache.spark.scheduler.cluster.{YarnClientClusterScheduler, YarnClusterScheduler}
import org.apache.spark.SparkContext

// Client mode scheduler
val sc = new SparkContext(sparkConf)
val clientScheduler = new YarnClientClusterScheduler(sc)

// Cluster mode scheduler  
val clusterScheduler = new YarnClusterScheduler(sc)

// Client Mode Flow:
// SparkContext (Client Machine)
//     ↓
// YarnClientSchedulerBackend  
//     ↓
// YarnClientClusterScheduler
//     ↓  
// ApplicationMaster (YARN Cluster) - ExecutorLauncher mode
//     ↓
// Executor Containers (YARN Nodes)

// Cluster Mode Flow:
// ApplicationMaster (YARN Cluster) - Driver mode
//     ↓
// YarnClusterSchedulerBackend
//     ↓
// YarnClusterScheduler  
//     ↓
// Executor Containers (YARN Nodes)

// Scheduler backends integrate with YARN resource management
abstract class YarnSchedulerBackend {
  // Request executor containers from YARN
  protected def requestExecutors(numExecutors: Int): Unit
  
  // Handle executor container allocation from ResourceManager
  protected def onExecutorsAdded(executorIds: Seq[String]): Unit
  
  // Handle executor failures and cleanup
  protected def onExecutorRemoved(executorId: String, reason: String): Unit
}

// Configuration for dynamic executor allocation
spark.dynamicAllocation.enabled=true
spark.dynamicAllocation.minExecutors=1  
spark.dynamicAllocation.maxExecutors=10
spark.dynamicAllocation.initialExecutors=2

// YarnSchedulerBackend handles dynamic scaling
// Requests/releases executors based on workload

// Key configuration properties for client mode
spark.yarn.am.memory=512m              // ApplicationMaster memory
spark.yarn.am.cores=1                  // ApplicationMaster cores
spark.yarn.am.waitTime=100s            // Max wait for SparkContext
spark.yarn.client.executor.graceTime=5s // Executor shutdown grace period

// Key configuration properties for cluster mode  
spark.driver.memory=1g                 // Driver memory (runs in AM)
spark.driver.cores=1                   // Driver cores
spark.yarn.driver.memoryFraction=0.1   // Driver memory fraction
spark.yarn.am.extraJavaOptions         // JVM options for AM/driver

// Configuration affecting both modes
spark.executor.memory=1g               // Executor memory
spark.executor.cores=1                 // Executor cores
spark.executor.instances=2             // Number of executors
spark.yarn.queue=default               // YARN queue
spark.yarn.priority=0                  // Application priority

// Scheduler backends provide fault tolerance mechanisms
class YarnSchedulerBackend {
  // Detect executor failures through heartbeat monitoring
  // Automatically request replacement executors from YARN
  // Reschedule failed tasks on healthy executors
  // Blacklist problematic nodes after repeated failures
}

// Scheduler backends optimize for data locality
class YarnClusterScheduler {
  // Prefer scheduling tasks on nodes with cached data
  // Consider HDFS block locations for task placement
  // Balance between locality and resource availability
}

// Efficient resource utilization strategies
class YarnSchedulerBackend {
  // Container sharing between multiple executors (when supported)
  // Optimal container size calculation based on workload
  // Preemption handling for shared cluster environments
}