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# Scheduler Backends
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Scheduler backend implementations for integrating Spark's TaskScheduler with YARN resource management. These components provide the bridge between Spark's task scheduling system and YARN's resource allocation, supporting both client and cluster deployment modes.
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## Capabilities
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### YarnClientSchedulerBackend
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Scheduler backend for YARN client mode, where the Spark driver runs on the client machine and the ApplicationMaster manages only executors.
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```scala { .api }
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/**
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 * Scheduler backend for YARN client mode
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 * Manages executor lifecycle and resource requests through YARN ApplicationMaster
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 */
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private[spark] class YarnClientSchedulerBackend(
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  scheduler: TaskSchedulerImpl, 
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  sc: SparkContext
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) extends YarnSchedulerBackend {
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  /**
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   * Start the backend and submit application to YARN
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   * Initiates ApplicationMaster and begins executor allocation
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   */
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  def start(): Unit
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  // Application monitoring and lifecycle management
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  // Resource request handling through ApplicationMaster
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  // Executor status tracking and failure handling
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}
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```
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**Usage Examples:**
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```scala
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import org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend
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import org.apache.spark.scheduler.TaskSchedulerImpl
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import org.apache.spark.SparkContext
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// Typically created by Spark runtime in client mode
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val sc = new SparkContext(sparkConf)
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val taskScheduler = new TaskSchedulerImpl(sc)
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val backend = new YarnClientSchedulerBackend(taskScheduler, sc)
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// Backend lifecycle managed by Spark runtime
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backend.start()
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```
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### YarnClusterSchedulerBackend
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Scheduler backend for YARN cluster mode, where both the driver and executors run on the YARN cluster.
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```scala { .api }
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/**
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 * Scheduler backend for YARN cluster mode  
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 * Manages executors when driver runs within ApplicationMaster
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 */
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private[spark] class YarnClusterSchedulerBackend extends YarnSchedulerBackend {
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  // Cluster-specific resource management
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  // Direct integration with ApplicationMaster
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  // Optimized executor allocation for cluster deployment
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}
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```
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**Usage Examples:**
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```scala
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import org.apache.spark.scheduler.cluster.YarnClusterSchedulerBackend
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// Created by Spark runtime in cluster mode
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// Driver runs inside ApplicationMaster on YARN cluster
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val backend = new YarnClusterSchedulerBackend()
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```
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### YarnSchedulerBackend (Base Class)
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Abstract base class providing common YARN scheduler backend functionality.
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```scala { .api }
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/**
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 * Base class for YARN scheduler backend implementations
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 * Provides common functionality for both client and cluster modes
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 */
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private[spark] abstract class YarnSchedulerBackend 
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    extends CoarseGrainedSchedulerBackend {
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  // Common YARN resource management operations
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  // Executor container lifecycle management  
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  // Resource request and allocation handling
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  // Integration with YARN ApplicationMaster
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}
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```
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### Task Schedulers
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Task scheduler implementations optimized for YARN deployment modes.
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```scala { .api }
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/**
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 * Task scheduler for YARN client mode
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 * Optimized for client-side driver with remote executors
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 */
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private[spark] class YarnClientClusterScheduler(sc: SparkContext) 
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    extends TaskSchedulerImpl {
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  // Client-specific scheduling optimizations
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  // Remote executor communication handling
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  // Task distribution strategies for client mode
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}
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/**
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 * Task scheduler for YARN cluster mode
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 * Optimized for driver and executors running on same cluster  
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 */
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private[spark] class YarnClusterScheduler(sc: SparkContext) 
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    extends TaskSchedulerImpl {
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  // Cluster-specific scheduling optimizations
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  // Locality-aware task placement
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  // Efficient intra-cluster communication
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}
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```
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**Usage Examples:**
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```scala
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import org.apache.spark.scheduler.cluster.{YarnClientClusterScheduler, YarnClusterScheduler}
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import org.apache.spark.SparkContext
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// Client mode scheduler
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val sc = new SparkContext(sparkConf)
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val clientScheduler = new YarnClientClusterScheduler(sc)
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// Cluster mode scheduler  
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val clusterScheduler = new YarnClusterScheduler(sc)
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```
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## Scheduler Backend Architecture
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### Client Mode Architecture
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```scala
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// Client Mode Flow:
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// SparkContext (Client Machine)
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//     ↓
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// YarnClientSchedulerBackend  
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//     ↓
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// YarnClientClusterScheduler
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//     ↓  
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// ApplicationMaster (YARN Cluster) - ExecutorLauncher mode
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//     ↓
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// Executor Containers (YARN Nodes)
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```
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In client mode:
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1. Driver runs on client machine
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2. `YarnClientSchedulerBackend` submits ApplicationMaster to YARN
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3. ApplicationMaster runs in ExecutorLauncher mode (manages only executors)
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4. Tasks are scheduled from client to remote executors
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### Cluster Mode Architecture  
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```scala
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// Cluster Mode Flow:
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// ApplicationMaster (YARN Cluster) - Driver mode
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//     ↓
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// YarnClusterSchedulerBackend
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//     ↓
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// YarnClusterScheduler  
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//     ↓
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// Executor Containers (YARN Nodes)
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```
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In cluster mode:
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1. Driver runs inside ApplicationMaster on YARN cluster
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2. `YarnClusterSchedulerBackend` manages local executor allocation
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3. Optimized for locality and reduced network overhead
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## Resource Management Integration
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### Executor Allocation
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```scala
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// Scheduler backends integrate with YARN resource management
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abstract class YarnSchedulerBackend {
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  // Request executor containers from YARN
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  protected def requestExecutors(numExecutors: Int): Unit
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  // Handle executor container allocation from ResourceManager
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  protected def onExecutorsAdded(executorIds: Seq[String]): Unit
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  // Handle executor failures and cleanup
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  protected def onExecutorRemoved(executorId: String, reason: String): Unit
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}
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```
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### Dynamic Allocation
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Integration with Spark's dynamic allocation feature:
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```scala
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// Configuration for dynamic executor allocation
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spark.dynamicAllocation.enabled=true
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spark.dynamicAllocation.minExecutors=1  
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spark.dynamicAllocation.maxExecutors=10
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spark.dynamicAllocation.initialExecutors=2
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// YarnSchedulerBackend handles dynamic scaling
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// Requests/releases executors based on workload
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```
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## Configuration Options
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### Client Mode Configuration
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```scala
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// Key configuration properties for client mode
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spark.yarn.am.memory=512m              // ApplicationMaster memory
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spark.yarn.am.cores=1                  // ApplicationMaster cores
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spark.yarn.am.waitTime=100s            // Max wait for SparkContext
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spark.yarn.client.executor.graceTime=5s // Executor shutdown grace period
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```
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### Cluster Mode Configuration
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```scala
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// Key configuration properties for cluster mode  
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spark.driver.memory=1g                 // Driver memory (runs in AM)
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spark.driver.cores=1                   // Driver cores
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spark.yarn.driver.memoryFraction=0.1   // Driver memory fraction
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spark.yarn.am.extraJavaOptions         // JVM options for AM/driver
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```
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### Common Configuration
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```scala
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// Configuration affecting both modes
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spark.executor.memory=1g               // Executor memory
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spark.executor.cores=1                 // Executor cores
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spark.executor.instances=2             // Number of executors
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spark.yarn.queue=default               // YARN queue
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spark.yarn.priority=0                  // Application priority
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```
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## Fault Tolerance
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### Executor Failure Handling
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```scala
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// Scheduler backends provide fault tolerance mechanisms
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class YarnSchedulerBackend {
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  // Detect executor failures through heartbeat monitoring
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  // Automatically request replacement executors from YARN
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  // Reschedule failed tasks on healthy executors
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  // Blacklist problematic nodes after repeated failures
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}
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```
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### ApplicationMaster Failure Recovery
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- **Client Mode**: ApplicationMaster failure requires resubmission
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- **Cluster Mode**: Driver failure terminates application (no recovery)
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- Both modes support checkpoint-based recovery for stateful applications
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## Performance Optimization
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### Locality Optimization
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```scala
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// Scheduler backends optimize for data locality
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class YarnClusterScheduler {
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  // Prefer scheduling tasks on nodes with cached data
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  // Consider HDFS block locations for task placement
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  // Balance between locality and resource availability
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}
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```
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### Resource Utilization
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```scala
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// Efficient resource utilization strategies
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class YarnSchedulerBackend {
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  // Container sharing between multiple executors (when supported)
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  // Optimal container size calculation based on workload
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  // Preemption handling for shared cluster environments
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}
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```
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## Monitoring and Metrics
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The scheduler backends integrate with Spark's metrics system:
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- Executor allocation/deallocation events
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- Task scheduling latency metrics  
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- Resource utilization tracking
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- YARN application progress reporting
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- Integration with Spark History Server