attilapiros commented on a change in pull request #29014:
URL: https://github.com/apache/spark/pull/29014#discussion_r460165492



##########
File path: 
core/src/test/scala/org/apache/spark/deploy/DecommissionWorkerSuite.scala
##########
@@ -0,0 +1,401 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.deploy
+
+import java.util.concurrent.{ConcurrentHashMap, ConcurrentLinkedQueue}
+import java.util.concurrent.atomic.AtomicBoolean
+
+import scala.collection.JavaConverters._
+import scala.collection.mutable
+import scala.collection.mutable.ArrayBuffer
+import scala.concurrent.duration._
+
+import org.scalatest.BeforeAndAfterEach
+import org.scalatest.concurrent.Eventually._
+import org.scalatest.time.Span
+
+import org.apache.spark._
+import org.apache.spark.deploy.DeployMessages.{MasterStateResponse, 
RequestMasterState, WorkerDecommission}
+import org.apache.spark.deploy.master.{ApplicationInfo, Master, WorkerInfo}
+import org.apache.spark.deploy.worker.Worker
+import org.apache.spark.internal.{config, Logging}
+import org.apache.spark.network.TransportContext
+import org.apache.spark.network.netty.SparkTransportConf
+import org.apache.spark.network.shuffle.ExternalBlockHandler
+import org.apache.spark.rpc.{RpcAddress, RpcEnv}
+import org.apache.spark.scheduler.{SparkListener, SparkListenerJobEnd, 
SparkListenerStageSubmitted, SparkListenerTaskEnd, SparkListenerTaskStart, 
TaskInfo}
+import org.apache.spark.shuffle.FetchFailedException
+import org.apache.spark.storage.BlockManagerId
+import org.apache.spark.util.Utils
+
+class DecommissionWorkerSuite
+  extends SparkFunSuite
+    with Logging
+    with LocalSparkContext
+    with BeforeAndAfterEach {
+
+  private val conf = new SparkConf()
+  private val securityManager = new SecurityManager(conf)
+
+  private var masterRpcEnv: RpcEnv = null
+  private var master: Master = null
+  private val workerIdToRpcEnvs: mutable.HashMap[String, RpcEnv] = 
mutable.HashMap.empty
+  private val workers: mutable.ArrayBuffer[Worker] = mutable.ArrayBuffer.empty
+
+  override def beforeEach(): Unit = {
+    super.beforeEach()
+    masterRpcEnv = RpcEnv.create(Master.SYSTEM_NAME, "localhost", 0, conf, 
securityManager)
+    master = makeMaster()
+  }
+
+  override def afterEach(): Unit = {
+    try {
+      masterRpcEnv.shutdown()
+      workerIdToRpcEnvs.values.foreach(_.shutdown())
+      workerIdToRpcEnvs.clear()
+      master.stop()
+      workers.foreach(_.stop())
+      workers.clear()
+      masterRpcEnv = null
+    } finally {
+      super.afterEach()
+    }
+  }
+
+  test("decommission workers should not result in job failure") {
+    val maxTaskFailures = conf.get(config.TASK_MAX_FAILURES)
+    val numTimesToKillWorkers = maxTaskFailures + 1
+    val numWorkers = numTimesToKillWorkers + 1
+    makeWorkers(numWorkers)
+
+    // Here we will have a single task job and we will keep decommissioning 
(and killing) the
+    // worker running that task K times. Where K is more than the 
maxTaskFailures. Since the worker
+    // is notified of the decommissioning, the task failures can be ignored 
and not fail
+    // the job.
+
+    sc = createSparkContext(appConf)
+    val executorIdToWorkerInfo = getExecutorToWorkerAssignments
+    val taskIdsKilled = new ConcurrentHashMap[Long, Boolean]
+    val listener = new RootStageAwareListener {
+      override def handleRootTaskStart(taskStart: SparkListenerTaskStart): 
Unit = {
+        val taskInfo = taskStart.taskInfo
+        assert(taskInfo.index == 0, s"Unknown task index ${taskInfo.index}")
+        if (taskIdsKilled.size() < numTimesToKillWorkers) {
+          val workerInfo = executorIdToWorkerInfo(taskInfo.executorId)
+          decommissionWorkerOnMaster(workerInfo, "partition 0 must die")
+          killWorkerAfterTimeout(workerInfo, 1)
+          taskIdsKilled.put(taskInfo.taskId, true)
+        }
+      }
+
+      override def handleRootTaskEnd(taskEnd: SparkListenerTaskEnd): Unit = {
+        val taskInfo = taskEnd.taskInfo
+        assert(taskInfo.index === 0, s"Unknown task index ${taskInfo.index}")
+        // If a task has been killed then it shouldn't be successful
+        assert(taskInfo.successful === 
!taskIdsKilled.getOrDefault(taskInfo.taskId, false))
+      }
+    }
+    sc.addSparkListener(listener)
+    // single task job
+    val jobResult = sc.parallelize(1 to 1, 1).map(_ => {
+      Thread.sleep(5 * 1000L); 1
+    }).count()
+    assert(jobResult === 1)
+  }
+
+  test("decommission workers ensure that shuffle output is regenerated even 
with shuffle service") {
+    val conf = appConf
+    conf.set(config.Tests.TEST_NO_STAGE_RETRY, true)
+    conf.set(config.SHUFFLE_MANAGER, "sort")
+    conf.set(config.SHUFFLE_SERVICE_ENABLED, true)
+    makeWorkers(2)
+    val ss = new ExternalShuffleServiceHolder(conf)
+    sc = createSparkContext(conf)
+
+    // Here we will create a 2 stage job: The first stage will have two tasks 
and the second stage
+    // will have one task. The two tasks in the first stage will be long and 
short. We decommission
+    // and kill the worker after the short task is done. Eventually the driver 
should get the
+    // executor lost signal for the short task executor. This should trigger 
regenerating
+    // the shuffle output since we cleanly decommissioned the executor, 
despite running with an
+    // external shuffle service.
+    try {
+      val executorIdToWorkerInfo = getExecutorToWorkerAssignments
+      val task0Killed = new AtomicBoolean(false)
+      // single task job
+      val listener = new RootStageAwareListener {
+        override def handleRootTaskEnd(taskEnd: SparkListenerTaskEnd): Unit = {
+          val taskInfo = taskEnd.taskInfo
+          assert(taskInfo.index <= 1, s"Unknown task index ${taskInfo.index}")
+          if (taskInfo.index == 0) {
+            if (task0Killed.compareAndSet(false, true)) {
+              assert(taskInfo.successful)
+              // Since this task hasn't been killed before, it should still be 
at its first attempt.
+              assert(taskInfo.attemptNumber === 0)
+              val workerInfo = executorIdToWorkerInfo(taskInfo.executorId)
+              decommissionWorkerOnMaster(workerInfo, "Kill early done map 
worker")
+              killWorkerAfterTimeout(workerInfo, 0)
+              logInfo(s"Killed the node ${workerInfo.hostPort} that was 
running the early task")
+            } else {
+              assert(taskInfo.successful)
+              // The first attempt of this task should have failed since it 
was killed. Thus,
+              // either the task attempt or the stage attempt number should be 
more than 0.
+              assert(taskInfo.attemptNumber > 0 || taskEnd.stageAttemptId > 0)
+            }
+          } else {
+            // The second task is never touched and thus should succeed on the 
first attempt.
+            assert(taskInfo.successful)
+            assert(taskInfo.attemptNumber === 0)
+          }
+        }
+      }
+      sc.addSparkListener(listener)
+      val jobResult = sc.parallelize(1 to 2, 2).mapPartitionsWithIndex((pid, 
iter) => {
+        val sleepTimeSeconds = if (pid == 0) 1 else 10
+        Thread.sleep(sleepTimeSeconds * 1000L)
+        List.fill(pid + 1)(pid * 2 + 1).iterator
+      }, preservesPartitioning = true).repartition(1).sum()
+      assert(jobResult > 1)

Review comment:
       Do you expect here a result changing by different runs? i mean why not 
an equality with a constant is given?  
   If there is a reason for this please mention it in a comment above.




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