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Create central manager to track memory for all spilling collections
This tracks memory properly if there are multiple spilling collections in the same task (which was a problem before), and also implements an algorithm that lets each thread grow up to 1 / 2N of the memory pool (where N is the number of threads) before spilling, which avoids an inefficiency with small spills we had before (some threads would spill many times at 0-1 MB because the pool was allocated elsewhere).
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core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala
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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.shuffle | ||
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| import org.apache.spark.{SparkException, SparkConf} | ||
| import scala.collection.mutable | ||
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| /** | ||
| * Allocates a pool of memory to task threads for use in shuffle operations. Each disk-spilling | ||
| * collection (ExternalAppendOnlyMap or ExternalSorter) used by these tasks can acquire memory | ||
| * from this pool and release it as it spills data out. When a task ends, all its memory will be | ||
| * released by the Executor. | ||
| * | ||
| * This class tries to ensure that each thread gets a reasonable share of memory, instead of some | ||
| * thread ramping up to a large amount first and then causing others to spill to disk repeatedly. | ||
| * If there are N threads, it ensures that each thread can acquire at least 1 / 2N of the memory | ||
| * before it has to spill, and at most 1 / N. Because N varies dynamically, we keep track of the | ||
| * set of active threads and redo the calculations of 1 / 2N and 1 / N in waiting threads whenever | ||
| * this set changes. This is all done by synchronizing access on "this" to mutate state and using | ||
| * wait() and notifyAll() to signal changes. | ||
| */ | ||
| private[spark] class ShuffleMemoryManager(maxMemory: Long) { | ||
| private val threadMemory = new mutable.HashMap[Long, Long]() // threadId -> memory bytes | ||
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| def this(conf: SparkConf) = this(ShuffleMemoryManager.getMaxMemory(conf)) | ||
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| /** | ||
| * Try to acquire numBytes memory for the current thread, or return false if the pool cannot | ||
| * allocate this much memory to it. This call may block until there is enough free memory in | ||
| * some situations, to make sure each thread has a chance to ramp up to a reasonable share of | ||
| * the available memory before being forced to spill. | ||
| */ | ||
| def tryToAcquire(numBytes: Long): Boolean = synchronized { | ||
| val threadId = Thread.currentThread().getId | ||
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| // Add this thread to the threadMemory map just so we can keep an accurate count of the number | ||
| // of active threads, to let other threads ramp down their memory in calls to tryToAcquire | ||
| threadMemory.getOrElseUpdate(threadId, 0L) | ||
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| // Keep looping until we're either sure that we don't want to grant this request (because this | ||
| // thread would have more than 1 / numActiveThreads of the memory) or we have enough free | ||
| // memory to give it (we always let each thread get at least 1 / (2 * numActiveThreads)). | ||
| while (true) { | ||
| val numActiveThreads = threadMemory.keys.size | ||
| val curMem = threadMemory(threadId) | ||
| if (curMem + numBytes > maxMemory / numActiveThreads) { | ||
| // We'd get more than 1 / numActiveThreads of the total memory; don't allow that | ||
| return false | ||
| } | ||
| val bytesFree = maxMemory - threadMemory.values.sum | ||
| if (bytesFree >= numBytes) { | ||
| // Grant the request | ||
| threadMemory(threadId) = curMem + numBytes | ||
| // Notify other waiting threads because the # active of threads may have increased, so | ||
| // they may cancel their current waits | ||
| notifyAll() | ||
| return true | ||
| } else if (curMem + numBytes <= maxMemory / (2 * numActiveThreads)) { | ||
| // This thread has so little memory that we want it to block and acquire a bigger | ||
| // amount instead of cancelling the request. Wait on "this" for a thread to call notify. | ||
| // Before doing the wait, however, also notify other current waiters in case our thread | ||
| // becoming active just pushed them over the limit to give up their own waits. | ||
| notifyAll() | ||
| wait() | ||
| } else { | ||
| // Thread would have between 1 / (2 * numActiveThreads) and 1 / numActiveThreads memory | ||
| return false | ||
| } | ||
| } | ||
| false // Never reached | ||
| } | ||
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| /** Release numBytes bytes for the current thread. */ | ||
| def release(numBytes: Long): Unit = synchronized { | ||
| val threadId = Thread.currentThread().getId | ||
| val curMem = threadMemory.getOrElse(threadId, 0L) | ||
| if (curMem < numBytes) { | ||
| throw new SparkException( | ||
| s"Internal error: release called on ${numBytes} bytes but thread only has ${curMem}") | ||
| } | ||
| threadMemory(threadId) -= numBytes | ||
| notifyAll() // Notify waiters who locked "this" in tryToAcquire that memory has freed | ||
| } | ||
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| /** Release all memory for the current thread and mark it as inactive (e.g. when a task ends). */ | ||
| def releaseMemoryForThisThread(): Unit = synchronized { | ||
| val threadId = Thread.currentThread().getId | ||
| threadMemory.remove(threadId) | ||
| notifyAll() // Notify waiters who locked "this" in tryToAcquire that memory has freed | ||
| } | ||
| } | ||
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| private object ShuffleMemoryManager { | ||
| def getMaxMemory(conf: SparkConf): Long = { | ||
| val memoryFraction = conf.getDouble("spark.shuffle.memoryFraction", 0.2) | ||
| val safetyFraction = conf.getDouble("spark.shuffle.safetyFraction", 0.8) | ||
| (Runtime.getRuntime.maxMemory * memoryFraction * safetyFraction).toLong | ||
| } | ||
| } |
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