attilapiros commented on a change in pull request #29014: URL: https://github.com/apache/spark/pull/29014#discussion_r460238860
########## 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) + // 4 tasks: 2 from first stage, one retry due to decom, one more in the second stage. + val tasksSeen = listener.getTasksFinished(10.seconds) + 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) + makeWorkers(2) + sc = createSparkContext(conf) + val executorIdToWorkerInfo = getExecutorToWorkerAssignments + val executorToDecom = executorIdToWorkerInfo.keysIterator.next + val workerToDecomInfo = executorIdToWorkerInfo(executorToDecom) + val workerToDecomHost = workerToDecomInfo.host + val workerToDecomPort = workerToDecomInfo.port + // 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 decom 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(workerToDecomInfo, + "decommission worker after task on it is done") + } + } + } + sc.addSparkListener(listener) + val jobResult = sc.parallelize(1 to 2, 2).mapPartitionsWithIndex((index, _) => { Review comment: This repeats at several other places. Style guide I use: https://github.com/databricks/scala-style-guide#anonymous-methods ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org --------------------------------------------------------------------- To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org