Support eagerly kill redundant executors #4592
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| /* | ||
| * 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. | ||
| */ | ||
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| package org.apache.spark | ||
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| import scala.annotation.tailrec | ||
| import scala.collection.mutable | ||
| import scala.collection.mutable.ArrayBuffer | ||
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| import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend | ||
| import org.apache.spark.sql.SparkSession | ||
| import org.apache.spark.sql.catalyst.rules.Rule | ||
| import org.apache.spark.sql.execution.{FilterExec, ProjectExec, SortExec, SparkPlan} | ||
| import org.apache.spark.sql.execution.adaptive._ | ||
| import org.apache.spark.sql.execution.exchange.{ENSURE_REQUIREMENTS, ShuffleExchangeExec} | ||
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| import org.apache.kyuubi.sql.{KyuubiSQLConf, MarkNumOutputColumnsRule} | ||
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| /** | ||
| * This rule assumes the final write stage has less cores requirement than previous, otherwise | ||
| * this rule would take no effect. | ||
| * | ||
| * It provide a feature: | ||
| * 1. Kill redundant executors before running final write stage | ||
| */ | ||
| case class FinalStageResourceManager(session: SparkSession) extends Rule[SparkPlan] { | ||
| override def apply(plan: SparkPlan): SparkPlan = { | ||
| if (!conf.getConf(KyuubiSQLConf.FINAL_WRITE_STAGE_EAGERLY_KILL_EXECUTORS_ENABLED)) { | ||
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| return plan | ||
| } | ||
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| if (!MarkNumOutputColumnsRule.isWrite(session, plan)) { | ||
| return plan | ||
| } | ||
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| val sc = session.sparkContext | ||
| val dra = sc.getConf.getBoolean("spark.dynamicAllocation.enabled", false) | ||
| val coresPerExecutor = sc.getConf.getInt("spark.executor.cores", 1) | ||
| val minExecutors = sc.getConf.getInt("spark.dynamicAllocation.minExecutors", 0) | ||
| val maxExecutors = sc.getConf.getInt("spark.dynamicAllocation.maxExecutors", Int.MaxValue) | ||
| val factor = conf.getConf(KyuubiSQLConf.FINAL_WRITE_STAGE_PARTITION_FACTOR) | ||
| val hasImprovementRoom = maxExecutors - 1 > minExecutors * factor | ||
| // Fast fail if: | ||
| // 1. DRA off | ||
| // 2. only work with yarn and k8s | ||
| // 3. maxExecutors is not bigger than minExecutors * factor | ||
| if (!dra || !sc.schedulerBackend.isInstanceOf[CoarseGrainedSchedulerBackend] || | ||
| !hasImprovementRoom) { | ||
| return plan | ||
| } | ||
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| val stage = findFinalRebalanceStage(plan) | ||
| if (stage.isEmpty) { | ||
| return plan | ||
| } | ||
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| // Since we are in `prepareQueryStage`, the AQE shuffle read has not been applied. | ||
| // So we need to apply it by self. | ||
| val shuffleRead = queryStageOptimizerRules.foldLeft(stage.get.asInstanceOf[SparkPlan]) { | ||
| case (latest, rule) => rule.apply(latest) | ||
| } | ||
| shuffleRead match { | ||
| case AQEShuffleReadExec(stage: ShuffleQueryStageExec, partitionSpecs) => | ||
| // The condition whether inject custom resource profile: | ||
| // - target executors < active executors | ||
| // - target executors > min executors | ||
| val numActiveExecutors = sc.getExecutorIds().length | ||
| val targetCores = partitionSpecs.length | ||
| val targetExecutors = (math.ceil(targetCores.toFloat / coresPerExecutor) * factor).toInt | ||
| .max(1) | ||
| val hasBenefits = targetExecutors < numActiveExecutors && targetExecutors > minExecutors | ||
| if (hasBenefits) { | ||
| val shuffleId = stage.plan.asInstanceOf[ShuffleExchangeExec].shuffleDependency.shuffleId | ||
| val numReduce = stage.plan.asInstanceOf[ShuffleExchangeExec].numPartitions | ||
| // Now, there is only a final rebalance stage waiting to execute and all tasks of previous | ||
| // stage are finished. Kill redundant existed executors eagerly so the tasks of final | ||
| // stage can be centralized scheduled. | ||
| killExecutors(sc, targetExecutors, shuffleId, numReduce) | ||
| } else { | ||
| logInfo(s"Has no benefits to kill executors or inject custom resource profile, " + | ||
| s"active executors: $numActiveExecutors, min executor: $minExecutors, " + | ||
| s"target executors: $targetExecutors.") | ||
| } | ||
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| case _ => | ||
| } | ||
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| plan | ||
| } | ||
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| /** | ||
| * The priority of kill executors follow: | ||
| * 1. kill executor who is younger than other (The older the JIT works better) | ||
| * 2. kill executor who produces less shuffle data first | ||
| */ | ||
| private def findExecutorToKill( | ||
| sc: SparkContext, | ||
| targetExecutors: Int, | ||
| shuffleId: Int, | ||
| numReduce: Int): Seq[String] = { | ||
| val tracker = SparkEnv.get.mapOutputTracker.asInstanceOf[MapOutputTrackerMaster] | ||
| val shuffleStatus = tracker.shuffleStatuses(shuffleId) | ||
| val executorToBlockSize = new mutable.HashMap[String, Long] | ||
| shuffleStatus.withMapStatuses { mapStatus => | ||
| mapStatus.foreach { status => | ||
| var i = 0 | ||
| var sum = 0L | ||
| while (i < numReduce) { | ||
| sum += status.getSizeForBlock(i) | ||
| i += 1 | ||
| } | ||
| executorToBlockSize.getOrElseUpdate(status.location.executorId, sum) | ||
| } | ||
| } | ||
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| val backend = sc.schedulerBackend.asInstanceOf[CoarseGrainedSchedulerBackend] | ||
| val executorsWithRegistrationTs = backend.getExecutorsWithRegistrationTs() | ||
| val existedExecutors = executorsWithRegistrationTs.keys.toSet | ||
| val expectedNumExecutorToKill = existedExecutors.size - targetExecutors | ||
| if (expectedNumExecutorToKill < 1) { | ||
| return Seq.empty | ||
| } | ||
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| val executorIdsToKill = new ArrayBuffer[String]() | ||
| // We first kill executor who does not hold shuffle block. It would happen because | ||
| // the last stage is running fast and finished in a short time. The existed executors are | ||
| // from previous stages that have not been killed by DRA, so we can not find it by tracking | ||
| // shuffle status. | ||
| // We should evict executors by their alive time first and retain all of executors which | ||
| // have better locality for shuffle block. | ||
| val numExecutorToKillWithNoShuffle = expectedNumExecutorToKill - executorToBlockSize.size | ||
| executorsWithRegistrationTs.toSeq.sortBy(_._2).foreach { case (id, _) => | ||
| if (executorIdsToKill.length < numExecutorToKillWithNoShuffle && | ||
| !executorToBlockSize.contains(id)) { | ||
| executorIdsToKill.append(id) | ||
| } | ||
| } | ||
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| // Evict the rest executors according to the shuffle block size | ||
| executorToBlockSize.toSeq.sortBy(_._2).foreach { case (id, _) => | ||
| if (executorIdsToKill.length < expectedNumExecutorToKill) { | ||
| executorIdsToKill.append(id) | ||
| } | ||
| } | ||
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| executorIdsToKill.toSeq | ||
| } | ||
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| private def killExecutors( | ||
|
Member
There was a problem hiding this comment. org.apache.spark.SparkContext#killExecutors?
Contributor
Author
There was a problem hiding this comment. There is a story about DRA. Since apache/spark#20604, |
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| sc: SparkContext, | ||
| targetExecutors: Int, | ||
| shuffleId: Int, | ||
| numReduce: Int): Unit = { | ||
| val executorAllocationClient = sc.schedulerBackend.asInstanceOf[ExecutorAllocationClient] | ||
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| val executorsToKill = findExecutorToKill(sc, targetExecutors, shuffleId, numReduce) | ||
| logInfo(s"Request to kill executors, total count ${executorsToKill.size}, " + | ||
| s"[${executorsToKill.mkString(", ")}].") | ||
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| // Note, `SparkContext#killExecutors` does not allow with ARD enabled, | ||
| // see `https://github.com/apache/spark/pull/20604`. | ||
| // It may cause the status in `ExecutorAllocationManager` inconsistent with | ||
| // `CoarseGrainedSchedulerBackend` for a while. But it should be synchronous finally. | ||
| executorAllocationClient.killExecutors( | ||
| executorIds = executorsToKill, | ||
| adjustTargetNumExecutors = false, | ||
| countFailures = false, | ||
| force = false) | ||
| } | ||
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| @tailrec | ||
| private def findFinalRebalanceStage(plan: SparkPlan): Option[ShuffleQueryStageExec] = { | ||
| plan match { | ||
| case p: ProjectExec => findFinalRebalanceStage(p.child) | ||
| case f: FilterExec => findFinalRebalanceStage(f.child) | ||
| case s: SortExec if !s.global => findFinalRebalanceStage(s.child) | ||
| case stage: ShuffleQueryStageExec | ||
| if stage.isMaterialized && | ||
| stage.plan.isInstanceOf[ShuffleExchangeExec] && | ||
| stage.plan.asInstanceOf[ShuffleExchangeExec].shuffleOrigin != ENSURE_REQUIREMENTS => | ||
| Some(stage) | ||
| case _ => None | ||
| } | ||
| } | ||
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| @transient private val queryStageOptimizerRules: Seq[Rule[SparkPlan]] = Seq( | ||
| OptimizeSkewInRebalancePartitions, | ||
| CoalesceShufflePartitions(session), | ||
| OptimizeShuffleWithLocalRead) | ||
| } | ||
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this is for unstable calls?
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yes, e.g.,
CoarseGrainedSchedulerBackendis underprivate[spark]