[jvm-packages] cancel job instead of killing SparkContext (#6019)

* cancel job instead of killing SparkContext

This PR changes the default behavior that kills SparkContext. Instead, This PR
cancels jobs when coming across task failed. That means the SparkContext is
still alive even some exceptions happen.

* add a parameter to control if killing SparkContext

* cancel the jobs the failed task belongs to

* remove the jobId from the map when one job failed.

* resolve comments

jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoost.scala

@@ -76,7 +76,8 @@ private[this] case class XGBoostExecutionParams(
     earlyStoppingParams: XGBoostExecutionEarlyStoppingParams,
     cacheTrainingSet: Boolean,
     treeMethod: Option[String],
-    isLocal: Boolean) {
+    isLocal: Boolean,
+    killSparkContextOnWorkerFailure: Boolean) {
 
   private var rawParamMap: Map[String, Any] = _
 
@@ -220,6 +221,9 @@ private[this] class XGBoostExecutionParamsFactory(rawParams: Map[String, Any], s
     val cacheTrainingSet = overridedParams.getOrElse("cache_training_set", false)
       .asInstanceOf[Boolean]
 
+    val killSparkContext = overridedParams.getOrElse("kill_spark_context_on_worker_failure", true)
+      .asInstanceOf[Boolean]
+
     val xgbExecParam = XGBoostExecutionParams(nWorkers, round, useExternalMemory, obj, eval,
       missing, allowNonZeroForMissing, trackerConf,
       timeoutRequestWorkers,
@@ -228,7 +232,8 @@ private[this] class XGBoostExecutionParamsFactory(rawParams: Map[String, Any], s
       xgbExecEarlyStoppingParams,
       cacheTrainingSet,
       treeMethod,
-      isLocal)
+      isLocal,
+      killSparkContext)
     xgbExecParam.setRawParamMap(overridedParams)
     xgbExecParam
   }
@@ -588,7 +593,8 @@ object XGBoost extends Serializable {
       val (booster, metrics) = try {
         val parallelismTracker = new SparkParallelismTracker(sc,
           xgbExecParams.timeoutRequestWorkers,
-          xgbExecParams.numWorkers)
+          xgbExecParams.numWorkers,
+          xgbExecParams.killSparkContextOnWorkerFailure)
         val rabitEnv = tracker.getWorkerEnvs
         val boostersAndMetrics = if (hasGroup) {
           trainForRanking(transformedTrainingData.left.get, xgbExecParams, rabitEnv, prevBooster,
@@ -628,7 +634,9 @@ object XGBoost extends Serializable {
       case t: Throwable =>
         // if the job was aborted due to an exception
         logger.error("the job was aborted due to ", t)
-        trainingData.sparkContext.stop()
+        if (xgbExecParams.killSparkContextOnWorkerFailure) {
+          trainingData.sparkContext.stop()
+        }
         throw t
     } finally {
       uncacheTrainingData(xgbExecParams.cacheTrainingSet, transformedTrainingData)

jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/LearningTaskParams.scala

@@ -105,8 +105,14 @@ private[spark] trait LearningTaskParams extends Params {
 
   final def getMaximizeEvaluationMetrics: Boolean = $(maximizeEvaluationMetrics)
 
-  setDefault(objective -> "reg:squarederror", baseScore -> 0.5,
+  /**
-    trainTestRatio -> 1.0, numEarlyStoppingRounds -> 0, cacheTrainingSet -> false)
+   * whether killing SparkContext when training task fails
+   */
+  final val killSparkContextOnWorkerFailure = new BooleanParam(this,
+    "killSparkContextOnWorkerFailure", "whether killing SparkContext when training task fails")
+
+  setDefault(objective -> "reg:squarederror", baseScore -> 0.5, trainTestRatio -> 1.0,
+    numEarlyStoppingRounds -> 0, cacheTrainingSet -> false, killSparkContextOnWorkerFailure -> true)
 }
 
 private[spark] object LearningTaskParams {

jvm-packages/xgboost4j-spark/src/main/scala/org/apache/spark/SparkParallelismTracker.scala

@@ -19,6 +19,8 @@ package org.apache.spark
 import org.apache.commons.logging.LogFactory
 import org.apache.spark.scheduler._
 
+import scala.collection.mutable.{HashMap, HashSet}
+
 /**
  * A tracker that ensures enough number of executor cores are alive.
  * Throws an exception when the number of alive cores is less than nWorkers.
@@ -26,11 +28,13 @@ import org.apache.spark.scheduler._
  * @param sc The SparkContext object
  * @param timeout The maximum time to wait for enough number of workers.
  * @param numWorkers nWorkers used in an XGBoost Job
+ * @param killSparkContextOnWorkerFailure kill SparkContext or not when task fails
  */
 class SparkParallelismTracker(
     val sc: SparkContext,
     timeout: Long,
-    numWorkers: Int) {
+    numWorkers: Int,
+    killSparkContextOnWorkerFailure: Boolean = true) {
 
   private[this] val requestedCores = numWorkers * sc.conf.getInt("spark.task.cpus", 1)
   private[this] val logger = LogFactory.getLog("XGBoostSpark")
@@ -58,7 +62,7 @@ class SparkParallelismTracker(
   }
 
   private[this] def safeExecute[T](body: => T): T = {
-    val listener = new TaskFailedListener
+    val listener = new TaskFailedListener(killSparkContextOnWorkerFailure)
     sc.addSparkListener(listener)
     try {
       body
@@ -79,7 +83,7 @@ class SparkParallelismTracker(
   def execute[T](body: => T): T = {
     if (timeout <= 0) {
       logger.info("starting training without setting timeout for waiting for resources")
-      body
+      safeExecute(body)
     } else {
       logger.info(s"starting training with timeout set as $timeout ms for waiting for resources")
       if (!waitForCondition(numAliveCores >= requestedCores, timeout)) {
@@ -90,16 +94,51 @@ class SparkParallelismTracker(
   }
 }
 
-private[spark] class TaskFailedListener extends SparkListener {
+class TaskFailedListener(killSparkContext: Boolean = true) extends SparkListener {
 
   private[this] val logger = LogFactory.getLog("XGBoostTaskFailedListener")
 
+  // {jobId, [stageId0, stageId1, ...] }
+  // keep track of the mapping of job id and stage ids
+  // when a task fails, find the job id and stage id the task belongs to, finally
+  // cancel the jobs
+  private val jobIdToStageIds: HashMap[Int, HashSet[Int]] = HashMap.empty
+
+  override def onJobStart(jobStart: SparkListenerJobStart): Unit = {
+    if (!killSparkContext) {
+      jobStart.stageIds.foreach(stageId => {
+        jobIdToStageIds.getOrElseUpdate(jobStart.jobId, new HashSet[Int]()) += stageId
+      })
+    }
+  }
+
+  override def onJobEnd(jobEnd: SparkListenerJobEnd): Unit = {
+    if (!killSparkContext) {
+      jobIdToStageIds.remove(jobEnd.jobId)
+    }
+  }
+
   override def onTaskEnd(taskEnd: SparkListenerTaskEnd): Unit = {
     taskEnd.reason match {
       case taskEndReason: TaskFailedReason =>
         logger.error(s"Training Task Failed during XGBoost Training: " +
-            s"$taskEndReason, stopping SparkContext")
+            s"$taskEndReason")
-        TaskFailedListener.startedSparkContextKiller()
+        if (killSparkContext) {
+          logger.error("killing SparkContext")
+          TaskFailedListener.startedSparkContextKiller()
+        } else {
+          val stageId = taskEnd.stageId
+          // find job ids according to stage id and then cancel the job
+
+          jobIdToStageIds.foreach {
+            case (jobId, stageIds) =>
+              if (stageIds.contains(stageId)) {
+                logger.error("Cancelling jobId:" + jobId)
+                jobIdToStageIds.remove(jobId)
+                SparkContext.getOrCreate().cancelJob(jobId)
+              }
+          }
+        }
       case _ =>
     }
   }

jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostRabitRegressionSuite.scala

@@ -116,4 +116,28 @@ class XGBoostRabitRegressionSuite extends FunSuite with PerTest {
       assert(waitAndCheckSparkShutdown(100) == true)
     }
   }
+
+  test("test SparkContext should not be killed ") {
+    val training = buildDataFrame(Classification.train)
+    // mock rank 0 failure during 8th allreduce synchronization
+    Rabit.mockList = Array("0,8,0,0").toList.asJava
+
+    try {
+      new XGBoostClassifier(Map(
+        "eta" -> "0.1",
+        "max_depth" -> "10",
+        "verbosity" -> "1",
+        "objective" -> "binary:logistic",
+        "num_round" -> 5,
+        "num_workers" -> numWorkers,
+        "kill_spark_context_on_worker_failure" -> false,
+        "rabit_timeout" -> 0))
+        .fit(training)
+    } catch {
+      case e: Throwable => // swallow anything
+    } finally {
+      // wait 3s to check if SparkContext is killed
+      assert(waitAndCheckSparkShutdown(3000) == false)
+    }
+  }
 }

jvm-packages/xgboost4j-spark/src/test/scala/org/apache/spark/SparkParallelismTrackerSuite.scala

@@ -34,6 +34,15 @@ class SparkParallelismTrackerSuite extends FunSuite with PerTest {
     .config("spark.driver.memory", "512m")
     .config("spark.task.cpus", 1)
 
+  private def waitAndCheckSparkShutdown(waitMiliSec: Int): Boolean = {
+    var totalWaitedTime = 0L
+    while (!ss.sparkContext.isStopped && totalWaitedTime <= waitMiliSec) {
+      Thread.sleep(100)
+      totalWaitedTime += 100
+    }
+    ss.sparkContext.isStopped
+  }
+
   test("tracker should not affect execution result when timeout is not larger than 0") {
     val nWorkers = numParallelism
     val rdd: RDD[Int] = sc.parallelize(1 to nWorkers)
@@ -74,4 +83,69 @@ class SparkParallelismTrackerSuite extends FunSuite with PerTest {
       }
     }
   }
+
+  test("tracker should not kill SparkContext when killSparkContextOnWorkerFailure=false") {
+    val nWorkers = numParallelism
+    val tracker = new SparkParallelismTracker(sc, 0, nWorkers, false)
+    val rdd: RDD[Int] = sc.parallelize(1 to nWorkers, nWorkers)
+    try {
+      tracker.execute {
+        rdd.map { i =>
+          val partitionId = TaskContext.get().partitionId()
+          if (partitionId == 0) {
+            throw new RuntimeException("mocking task failing")
+          }
+          i
+        }.sum()
+      }
+    } catch {
+      case e: Exception => // catch the exception
+    } finally {
+      // wait 3s to check if SparkContext is killed
+      assert(waitAndCheckSparkShutdown(3000) == false)
+    }
+  }
+
+  test("tracker should cancel the correct job when killSparkContextOnWorkerFailure=false") {
+    val nWorkers = 2
+    val tracker = new SparkParallelismTracker(sc, 0, nWorkers, false)
+    val rdd: RDD[Int] = sc.parallelize(1 to 10, nWorkers)
+    val thread = new TestThread(sc)
+    thread.start()
+    try {
+      tracker.execute {
+        rdd.map { i =>
+          Thread.sleep(100)
+          val partitionId = TaskContext.get().partitionId()
+          if (partitionId == 0) {
+            throw new RuntimeException("mocking task failing")
+          }
+          i
+        }.sum()
+      }
+    } catch {
+      case e: Exception => // catch the exception
+    } finally {
+      thread.join(8000)
+      // wait 3s to check if SparkContext is killed
+      assert(waitAndCheckSparkShutdown(3000) == false)
+    }
+  }
+
+  private[this] class TestThread(sc: SparkContext) extends Thread {
+    override def run(): Unit = {
+      var sum: Double = 0.0f
+      try {
+        val rdd = sc.parallelize(1 to 4, 2)
+        sum = rdd.mapPartitions(iter => {
+          // sleep 2s to ensure task is alive when cancelling other jobs
+          Thread.sleep(2000)
+          iter
+        }).sum()
+      } finally {
+        // get the correct result
+        assert(sum.toInt == 10)
+      }
+    }
+  }
 }