Github user vanzin commented on a diff in the pull request:

    https://github.com/apache/spark/pull/8007#discussion_r37354752
  
    --- Diff: 
core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala
 ---
    @@ -91,6 +92,68 @@ private[spark] abstract class YarnSchedulerBackend(
       }
     
       /**
    +   * Override the DriverEndpoint to add extra logic for the case when an 
executor is disconnected.
    +   * We should check the cluster manager and find if the loss of the 
executor was caused by YARN
    +   * force killing it due to preemption.
    +   */
    +  private class YarnDriverEndpoint(rpcEnv: RpcEnv, sparkProperties: 
ArrayBuffer[(String, String)])
    +      extends DriverEndpoint(rpcEnv, sparkProperties) {
    +
    +    private val pendingDisconnectedExecutors = new HashSet[String]
    +    private val handleDisconnectedExecutorThreadPool =
    +      
ThreadUtils.newDaemonCachedThreadPool("yarn-driver-handle-lost-executor-thread-pool")
    +
    +    /**
    +     * When onDisconnected is received at the driver endpoint, the 
superclass DriverEndpoint
    +     * handles it by assuming the Executor was lost for a bad reason and 
removes the executor
    +     * immediately.
    +     *
    +     * In YARN's case however it is crucial to talk to the application 
master and ask why the
    +     * executor had exited. In particular, the executor may have exited 
due to the executor
    +     * having been preempted. If the executor "exited normally" according 
to the application
    +     * master then we pass that information down to the TaskSetManager to 
inform the
    +     * TaskSetManager that tasks on that lost executor should not count 
towards a job failure.
    +     */
    +    override def onDisconnected(rpcAddress: RpcAddress): Unit = {
    +      addressToExecutorId.get(rpcAddress).foreach({ executorId =>
    --- End diff --
    
    style: `.foreach { executorId =>`


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