attilapiros commented on a change in pull request #29014: URL: https://github.com/apache/spark/pull/29014#discussion_r460520661
########## File path: core/src/test/scala/org/apache/spark/deploy/DecommissionWorkerSuite.scala ########## @@ -0,0 +1,424 @@ +/* + * 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().set(config.Worker.WORKER_DECOMMISSION_ENABLED, true) + 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 = 2 + conf.set(config.TASK_MAX_FAILURES, maxTaskFailures) + val numTimesToKillWorkers = maxTaskFailures + 1 + val numWorkers = numTimesToKillWorkers + 1 + createWorkers(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 + delayedAssert(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 + delayedAssert(taskInfo.index === 0, s"Expected task index ${taskInfo.index} to be 0") + // If a task has been killed then it shouldn't be successful + val taskSuccessExpected = !taskIdsKilled.getOrDefault(taskInfo.taskId, false) + val taskSuccessActual = taskInfo.successful + delayedAssert(taskSuccessActual === taskSuccessExpected, + s"Expected task success $taskSuccessActual == $taskSuccessExpected") + } + } + sc.addSparkListener(listener) + // single task job + val jobResult = sc.parallelize(1 to 1, 1).map { _ => + Thread.sleep(5 * 1000L); 1 + }.count() + assert(jobResult === 1) + assert(listener.getTasksFinished().size === numTimesToKillWorkers + 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) + createWorkers(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 workerForTask0Decommissioned = new AtomicBoolean(false) + // single task job + val listener = new RootStageAwareListener { + override def handleRootTaskEnd(taskEnd: SparkListenerTaskEnd): Unit = { + val taskInfo = taskEnd.taskInfo + delayedAssert(taskInfo.index <= 1, s"Expected ${taskInfo.index} <= 1") + delayedAssert(taskInfo.successful, s"Task ${taskInfo.index} should be successful") + if (taskInfo.index == 0) { + if (workerForTask0Decommissioned.compareAndSet(false, true)) { + // Since this task hasn't been killed before, it should still be at its first attempt. + delayedAssert(taskInfo.attemptNumber === 0, "Should have succeeded in 1st attempt") + val workerInfo = executorIdToWorkerInfo(taskInfo.executorId) + decommissionWorkerOnMaster(workerInfo, "Kill early done map worker") + killWorkerAfterTimeout(workerInfo, 0) + logInfo(s"Killed the node ${workerInfo.hostPort} that was running the early task") + } else { + // The task should have been rerun since the worker was decommissioned just after + // it was finished. + // either the task attempt or the stage attempt number should be more than 0. + val attemptNumber = taskInfo.attemptNumber + val stageAttempt = taskEnd.stageAttemptId + delayedAssert(attemptNumber > 0 || stageAttempt > 0, + s"The task should have been rerun after decommissioning worker:" + + s" ($attemptNumber, $stageAttempt)") + } + } else { + delayedAssert(taskInfo.attemptNumber === 0, "2nd task should succeed on 1st attempt") + } + } + } + sc.addSparkListener(listener) + val jobResult = sc.parallelize(1 to 2, 2).mapPartitionsWithIndex((pid, _) => { + val sleepTimeSeconds = if (pid == 0) 1 else 10 + Thread.sleep(sleepTimeSeconds * 1000L) + List(1).iterator + }, preservesPartitioning = true).repartition(1).sum() + assert(jobResult === 2) + // 4 tasks: 2 from first stage, one retry due to decom, one more in the second stage. + val tasksSeen = listener.getTasksFinished() + assert(tasksSeen.size >= 4, s"Expected at least 4 tasks but got $tasksSeen") + } finally { + ss.close() + } + } + + test("decommission workers ensure that fetch failures lead to rerun") { + val conf = appConf + conf.set(config.Tests.TEST_NO_STAGE_RETRY, false) + conf.set(config.UNREGISTER_OUTPUT_ON_HOST_ON_FETCH_FAILURE, true) + createWorkers(2) + sc = createSparkContext(conf) + val executorIdToWorkerInfo = getExecutorToWorkerAssignments + val executorToDecom = executorIdToWorkerInfo.keysIterator.next + + // The task code below cannot call executorIdToWorkerInfo, so we need to pre-compute + // the worker to decom to force it to be serialized into the task. + val workerToDecom = executorIdToWorkerInfo(executorToDecom) + + // The setup of this job is similar to the one above: 2 stage job with first stage having + // long and short tasks. Except that we want the shuffle output to be regenerated on a + // fetch failure instead of an executor lost. Since it is hard to "trigger a fetch failure", + // we manually raise the FetchFailed exception when the 2nd stage's task runs and require that + // fetch failure to trigger a recomputation. + logInfo(s"Will try to decommission the task running on executor $executorToDecom") + val listener = new RootStageAwareListener { + override def handleRootTaskEnd(taskEnd: SparkListenerTaskEnd): Unit = { + val taskInfo = taskEnd.taskInfo + if (taskInfo.executorId == executorToDecom && taskInfo.attemptNumber == 0 && + taskEnd.stageAttemptId == 0) { + decommissionWorkerOnMaster(workerToDecom, + "decommission worker after task on it is done") + } + } + } + sc.addSparkListener(listener) + val jobResult = sc.parallelize(1 to 2, 2).mapPartitionsWithIndex((_, _) => { + val executorId = SparkEnv.get.executorId + val sleepTimeSeconds = if (executorId == executorToDecom) 10 else 1 + Thread.sleep(sleepTimeSeconds * 1000L) + List(1).iterator + }, preservesPartitioning = true) + .repartition(1).mapPartitions(iter => { + val context = TaskContext.get() + if (context.attemptNumber == 0 && context.stageAttemptNumber() == 0) { + // MapIndex is explicitly -1 to force the entire host to be decommissioned + // However, this will cause both the tasks in the preceding stage since the host here is + // "localhost" (shortcoming of this single-machine unit test in that all the workers + // are actually on the same host) + throw new FetchFailedException(BlockManagerId(executorToDecom, + workerToDecom.host, workerToDecom.port), 0, 0, -1, 0, "Forcing fetch failure") + } + val sumVal: List[Int] = List(iter.sum) + sumVal.iterator + }, preservesPartitioning = true) + .sum() + assert(jobResult === 2) + // 6 tasks: 2 from first stage, 2 rerun again from first stage, 2nd stage attempt 1 and 2. + val tasksSeen = listener.getTasksFinished() + assert(tasksSeen.size === 6, s"Expected 6 tasks but got $tasksSeen") + } + + private abstract class RootStageAwareListener extends SparkListener { + protected var rootStageId: Option[Int] = None + var tasksFinished = new ConcurrentLinkedQueue[String]() + var jobDone = new AtomicBoolean(false) Review comment: Both `private` ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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