Github user brad-kaiser commented on a diff in the pull request: https://github.com/apache/spark/pull/19041#discussion_r156417466 --- Diff: core/src/main/scala/org/apache/spark/CacheRecoveryManager.scala --- @@ -0,0 +1,189 @@ +/* + * 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 + +import java.util.concurrent.{ConcurrentHashMap, ScheduledFuture, TimeUnit} + +import scala.collection.JavaConverters._ +import scala.collection.mutable +import scala.concurrent.{ExecutionContext, Future} +import scala.util.{Success => Succ} +import scala.util.Failure + +import org.apache.spark.internal.Logging +import org.apache.spark.internal.config.DYN_ALLOCATION_CACHE_RECOVERY_TIMEOUT +import org.apache.spark.rpc.RpcEndpointRef +import org.apache.spark.storage.{BlockManagerId, RDDBlockId} +import org.apache.spark.storage.BlockManagerMessages._ +import org.apache.spark.util.ThreadUtils + +/** + * Responsible for asynchronously replicating all of an executor's cached blocks, and then shutting + * it down. + */ +final private class CacheRecoveryManager( + blockManagerMasterEndpoint: RpcEndpointRef, + executorAllocationManager: ExecutorAllocationManager, + conf: SparkConf) + extends Logging { + + private val forceKillAfterS = conf.get(DYN_ALLOCATION_CACHE_RECOVERY_TIMEOUT) + private val threadPool = ThreadUtils.newDaemonCachedThreadPool("cache-recovery-manager-pool") + private implicit val asyncExecutionContext: ExecutionContext = + ExecutionContext.fromExecutorService(threadPool) + private val scheduler = + ThreadUtils.newDaemonSingleThreadScheduledExecutor("cache-recovery-shutdown-timers") + private val recoveringExecutors: mutable.Set[String] = + ConcurrentHashMap.newKeySet[String]().asScala + + /** + * Start the recover cache shutdown process for these executors + * + * @param execIds the executors to start shutting down + */ + def startCacheRecovery(execIds: Seq[String]): Unit = { + logDebug(s"Recover cached data before shutting down executors ${execIds.mkString(", ")}.") + val canBeRecovered = checkMem(execIds) + recoveringExecutors ++= canBeRecovered + val executorsWithKillTimers = canBeRecovered.zip(canBeRecovered.map(startKillTimer)) + executorsWithKillTimers.foreach((replicateUntilDone _).tupled) + } + + /** + * Given a list of executors that will be shut down, check if there is enough free memory on the + * rest of the cluster to hold their data. Return a list of just the executors for which there + * will be enough space. Executors are included smallest first. + * + * @param execIds executors which will be shut down + * @return a Seq of the executors we do have room for + */ + private def checkMem(execIds: Seq[String]): Seq[String] = { + val execsToShutDown = execIds.toSet + // Memory Status is a map of executor Id to a tuple of Max Memory and remaining memory on that + // executor. + val allExecMemStatus: Map[String, (Long, Long)] = blockManagerMasterEndpoint + .askSync[Map[BlockManagerId, (Long, Long)]](GetMemoryStatus) + .map { case (blockManagerId, mem) => blockManagerId.executorId -> mem } + + val (expiringMemStatus, remainingMemStatus) = allExecMemStatus.partition { + case (execId, _) => execsToShutDown.contains(execId) + } + val freeMemOnRemaining = remainingMemStatus.values.map(_._2).sum + + // The used mem on each executor sorted from least used mem to greatest + val executorAndUsedMem: Seq[(String, Long)] = + expiringMemStatus.map { case (execId, (maxMem, remainingMem)) => + val usedMem = maxMem - remainingMem + execId -> usedMem + }.toSeq.sortBy { case (_, usedMem) => usedMem } + + executorAndUsedMem + .scan(("start", freeMemOnRemaining)) { + case ((_, freeMem), (execId, usedMem)) => (execId, freeMem - usedMem) + } + .drop(1) + .filter { case (_, freeMem) => freeMem > 0 } --- End diff -- added comment
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