Resource Management

/**
 * Trait defining ResourceManager client interface
 * Provides abstraction for YARN ResourceManager communication
 */
trait YarnRMClient {
  // ResourceManager connection and authentication
  // Container resource requests and releases
  // Application status monitoring and reporting
  // Resource allocation callbacks and event handling
}

import org.apache.spark.deploy.yarn.YarnRMClient

// YarnRMClient implementations are version-specific
// Created internally by ApplicationMaster and Client components
val rmClient: YarnRMClient = // Implementation instance
// Used for ResourceManager communication throughout application lifecycle

/**
 * Implementation of YarnRMClient for specific YARN API versions
 * Available in both alpha (deprecated) and stable modules
 */
class YarnRMClientImpl(args: ApplicationMasterArguments) extends YarnRMClient {
  /**
   * Register ApplicationMaster with ResourceManager
   * @param host ApplicationMaster host
   * @param port ApplicationMaster port  
   * @param trackingUrl Application tracking URL
   * @return Registration response from ResourceManager
   */
  def register(host: String, port: Int, trackingUrl: String): RegisterApplicationMasterResponse
  
  /**
   * Allocate resources from ResourceManager
   * @param progressIndicator Application progress (0.0 to 1.0)
   * @return Allocation response with assigned containers
   */
  def allocate(progressIndicator: Float): AllocateResponse
  
  /**
   * Unregister ApplicationMaster from ResourceManager
   * @param status Final application status
   * @param appMessage Final message to ResourceManager
   * @param trackingUrl Final tracking URL
   */
  def unregister(status: FinalApplicationStatus, appMessage: String, trackingUrl: String): Unit
}

/**
 * Abstract base class for YARN resource allocation logic
 * Manages executor container requests and lifecycle
 */
private[yarn] abstract class YarnAllocator(
    conf: Configuration,
    sparkConf: SparkConf,
    appAttemptId: ApplicationAttemptId,
    args: ApplicationMasterArguments,
    preferredNodes: collection.Map[String, collection.Set[SplitInfo]],
    securityMgr: SecurityManager
) extends Logging {

  /** Abstract method to allocate containers from ResourceManager */
  protected def allocateContainers(resourceRequests: JList[ResourceRequest]): Unit
  
  /** Abstract method to release a specific container */
  protected def releaseContainer(container: Container): Unit
  
  /** Request total number of executors from cluster */
  def requestTotalExecutors(requestedTotal: Int): Unit
  
  /** Kill a specific executor by ID */
  def killExecutor(executorId: String): Unit
  
  /** Allocate resources and handle responses from ResourceManager */
  def allocateResources(): Unit
  
  /** Update resource requests for executor containers */
  def updateResourceRequests(): Unit
}

/**
 * Companion object with constants and utilities
 */
object YarnAllocator {
  // Internal constants for resource allocation
}

import org.apache.spark.deploy.yarn.YarnAllocator

// YarnAllocator is extended by version-specific implementations
// Handles the core logic for:
// - Requesting executor containers from ResourceManager
// - Launching executor processes in allocated containers  
// - Monitoring executor health and handling failures
// - Releasing containers when executors complete

/**
 * Enumeration for YARN allocation types
 * Defines resource allocation strategies for container placement
 */
object AllocationType extends Enumeration {
  type AllocationType = Value
  
  /** Host-specific allocation - request container on specific host */
  val HOST = Value
  
  /** Rack-specific allocation - request container within specific rack */
  val RACK = Value
  
  /** Any allocation - allow ResourceManager to place container anywhere */
  val ANY = Value
}

/**
 * Version-specific resource allocation implementation
 * Available in both alpha and stable API versions
 */
private[yarn] class YarnAllocationHandler extends YarnAllocator {
  // Concrete implementation of resource allocation logic
  // YARN API version-specific container operations
  // Executor container launch and monitoring
  // Resource optimization and failure recovery
}

/**
 * Enumeration for YARN allocation types
 * Defines different categories of resource requests
 */
object AllocationType extends Enumeration {
  // Different allocation type values for resource categorization
  // Used for tracking and managing different types of resource requests
}

import org.apache.spark.deploy.yarn.AllocationType

// Used internally for categorizing resource allocation requests
val allocationType = AllocationType.someValue
// Helps track and manage different types of container requests

// Typical resource allocation workflow
abstract class YarnAllocator {
  // 1. Calculate resource requirements based on Spark configuration
  protected def calculateResourceRequirements(): ContainerRequest
  
  // 2. Submit resource requests to ResourceManager
  protected def requestContainers(requests: List[ContainerRequest]): Unit
  
  // 3. Handle container allocation responses from ResourceManager
  protected def onContainersAllocated(containers: List[Container]): Unit
  
  // 4. Launch executor processes in allocated containers
  protected def launchExecutors(containers: List[Container]): Unit
  
  // 5. Monitor executor health and handle failures
  protected def monitorExecutors(): Unit
  
  // 6. Release containers when executors complete or fail
  protected def releaseContainers(containers: List[Container]): Unit
}

// Resource calculation based on Spark configuration
class YarnAllocator {
  private def buildContainerRequest(): ContainerRequest = {
    val executorMemory = sparkConf.get("spark.executor.memory", "1024m")
    val executorCores = sparkConf.getInt("spark.executor.cores", 1)
    val priority = sparkConf.getInt("spark.yarn.priority", 0)
    
    // Build YARN ContainerRequest with calculated resources
    // Include locality preferences and resource constraints
    // Set appropriate priority and capability requirements
  }
}

// Container launch process for executor deployment
class YarnAllocationHandler {
  private def launchExecutorContainer(container: Container): Unit = {
    // 1. Prepare executor launch context
    val launchContext = createExecutorLaunchContext()
    
    // 2. Set up environment variables and classpath
    setupExecutorEnvironment(launchContext)
    
    // 3. Configure executor JVM parameters
    configureExecutorJVM(launchContext)
    
    // 4. Launch container with NodeManager
    nodeManager.startContainer(container, launchContext)
    
    // 5. Track container for monitoring
    trackExecutorContainer(container.getId)
  }
}

// Continuous monitoring of executor containers
abstract class YarnAllocator {
  private def monitorContainerHealth(): Unit = {
    // Monitor executor heartbeats and health status
    // Detect container failures and exits  
    // Handle node failures and blacklisting
    // Report container status to ApplicationMaster
  }
}

// Optimizing container allocation for data locality
class YarnAllocator {
  private def buildLocalityRequest(
    preferredNodes: List[String],
    preferredRacks: List[String]
  ): ContainerRequest = {
    // Request containers on preferred nodes (highest priority)
    // Fall back to preferred racks (medium priority)  
    // Accept any available nodes (lowest priority)
    // Balance locality with resource availability
  }
}

// Dynamic executor allocation integration
abstract class YarnAllocator {
  def requestAdditionalExecutors(numExecutors: Int): Unit = {
    // Support for Spark's dynamic allocation feature
    // Scale executor count based on workload demands
    // Integrate with cluster resource availability
  }
  
  def removeExecutors(executorIds: Set[String]): Unit = {
    // Gracefully shutdown and release executor containers
    // Ensure running tasks complete before container release
    // Update resource tracking and allocation state
  }
}

// Key configuration properties affecting resource management
spark.executor.memory=2g                    // Executor container memory
spark.executor.cores=2                      // Executor CPU cores
spark.executor.memoryFraction=0.8          // Memory allocation within container
spark.executor.memoryOffHeap.enabled=true  // Off-heap memory usage

// YARN-specific resource configuration
spark.yarn.executor.memoryOverhead=384     // Additional memory overhead
spark.yarn.executor.nodeLabelExpression    // Node label constraints  
spark.yarn.priority=0                      // Application priority
spark.yarn.queue=production                // YARN queue assignment

// Container-level configuration options
spark.yarn.containerLauncherMaxThreads=25  // Parallel container launch limit
spark.yarn.executor.failuresValidityInterval=1h  // Failure tracking window
spark.yarn.max.executor.failures=3         // Max executor failures per node
spark.yarn.nodemanager.resource.memory-mb  // NodeManager memory limits

// Robust error handling for container failures
class YarnAllocator {
  private def handleContainerFailure(containerId: ContainerId, exitStatus: Int): Unit = {
    // Log container failure details
    // Determine if failure should trigger node blacklisting
    // Request replacement container if within failure limits
    // Report failure to Spark scheduler for task rescheduling
  }
  
  private def blacklistNode(nodeId: String, reason: String): Unit = {
    // Add node to blacklist to avoid future allocations
    // Implement exponential backoff for blacklist duration
    // Provide mechanisms for node rehabilitation
  }
}

// Handling ResourceManager failures and reconnection
trait YarnRMClient {
  private def handleRMFailure(): Unit = {
    // Detect ResourceManager connection loss
    // Implement reconnection with exponential backoff
    // Recover application state after reconnection
    // Resubmit pending resource requests
  }
}

// Integration with Spark metrics system
abstract class YarnAllocator {
  // Track container allocation success rates
  private val containerAllocationRate = metrics.meter("containerAllocationRate")
  
  // Monitor resource request fulfillment times
  private val resourceRequestLatency = metrics.histogram("resourceRequestLatency")
  
  // Track executor launch success and failure rates
  private val executorLaunchSuccess = metrics.counter("executorLaunchSuccess")
  private val executorLaunchFailure = metrics.counter("executorLaunchFailure")
}