Github user andrewor14 commented on a diff in the pull request: https://github.com/apache/spark/pull/1499#discussion_r15560695 --- Diff: core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala --- @@ -0,0 +1,667 @@ +/* + * 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.util.collection + +import java.io._ +import java.util.Comparator + +import scala.collection.mutable.ArrayBuffer +import scala.collection.mutable + +import com.google.common.io.ByteStreams + +import org.apache.spark.{Aggregator, SparkEnv, Logging, Partitioner} +import org.apache.spark.serializer.Serializer +import org.apache.spark.storage.BlockId + +/** + * Sorts and potentially merges a number of key-value pairs of type (K, V) to produce key-combiner + * pairs of type (K, C). Uses a Partitioner to first group the keys into partitions, and then + * optionally sorts keys within each partition using a custom Comparator. Can output a single + * partitioned file with a different byte range for each partition, suitable for shuffle fetches. + * + * If combining is disabled, the type C must equal V -- we'll cast the objects at the end. + * + * @param aggregator optional Aggregator with combine functions to use for merging data + * @param partitioner optional Partitioner; if given, sort by partition ID and then key + * @param ordering optional Ordering to sort keys within each partition; should be a total ordering + * @param serializer serializer to use when spilling to disk + * + * Note that if an Ordering is given, we'll always sort using it, so only provide it if you really + * want the output keys to be sorted. In a map task without map-side combine for example, you + * probably want to pass None as the ordering to avoid extra sorting. On the other hand, if you do + * want to do combining, having an Ordering is more efficient than not having it. + * + * At a high level, this class works as follows: + * + * - We repeatedly fill up buffers of in-memory data, using either a SizeTrackingAppendOnlyMap if + * we want to combine by key, or an simple SizeTrackingBuffer if we don't. Inside these buffers, + * we sort elements of type ((Int, K), C) where the Int is the partition ID. This is done to + * avoid calling the partitioner multiple times on the same key (e.g. for RangePartitioner). + * + * - When each buffer reaches our memory limit, we spill it to a file. This file is sorted first + * by partition ID and possibly second by key or by hash code of the key, if we want to do + * aggregation. For each file, we track how many objects were in each partition in memory, so we + * don't have to write out the partition ID for every element. + * + * - When the user requests an iterator, the spilled files are merged, along with any remaining + * in-memory data, using the same sort order defined above (unless both sorting and aggregation + * are disabled). If we need to aggregate by key, we either use a total ordering from the + * ordering parameter, or read the keys with the same hash code and compare them with each other + * for equality to merge values. + * + * - Users are expected to call stop() at the end to delete all the intermediate files. + */ +private[spark] class ExternalSorter[K, V, C]( + aggregator: Option[Aggregator[K, V, C]] = None, + partitioner: Option[Partitioner] = None, + ordering: Option[Ordering[K]] = None, + serializer: Option[Serializer] = None) extends Logging { + + private val numPartitions = partitioner.map(_.numPartitions).getOrElse(1) + private val shouldPartition = numPartitions > 1 + + private val blockManager = SparkEnv.get.blockManager + private val diskBlockManager = blockManager.diskBlockManager + private val ser = Serializer.getSerializer(serializer) + private val serInstance = ser.newInstance() + + private val conf = SparkEnv.get.conf + private val spillingEnabled = conf.getBoolean("spark.shuffle.spill", true) + private val fileBufferSize = conf.getInt("spark.shuffle.file.buffer.kb", 100) * 1024 + private val serializerBatchSize = conf.getLong("spark.shuffle.spill.batchSize", 10000) + + private def getPartition(key: K): Int = { + if (shouldPartition) partitioner.get.getPartition(key) else 0 + } + + // Data structures to store in-memory objects before we spill. Depending on whether we have an + // Aggregator set, we either put objects into an AppendOnlyMap where we combine them, or we + // store them in an array buffer. + var map = new SizeTrackingAppendOnlyMap[(Int, K), C] + var buffer = new SizeTrackingPairBuffer[(Int, K), C] + + // Number of pairs read from input since last spill; note that we count them even if a value is + // merged with a previous key in case we're doing something like groupBy where the result grows + private var elementsRead = 0L + + // What threshold of elementsRead we start estimating map size at. + private val trackMemoryThreshold = 1000 + + // Spilling statistics + private var spillCount = 0 + private var _memoryBytesSpilled = 0L + private var _diskBytesSpilled = 0L + + // Collective memory threshold shared across all running tasks + private val maxMemoryThreshold = { + val memoryFraction = conf.getDouble("spark.shuffle.memoryFraction", 0.2) + val safetyFraction = conf.getDouble("spark.shuffle.safetyFraction", 0.8) + (Runtime.getRuntime.maxMemory * memoryFraction * safetyFraction).toLong + } + + // How much of the shared memory pool this collection has claimed + private var myMemoryThreshold = 0L + + // A comparator for keys K that orders them within a partition to allow partial aggregation. + // Can be a partial ordering by hash code if a total ordering is not provided through by the + // user. (A partial ordering means that equal keys have comparator.compare(k, k) = 0, but some + // non-equal keys also have this, so we need to do a later pass to find truly equal keys). + // Note that we ignore this if no aggregator is given. + private val keyComparator: Comparator[K] = ordering.getOrElse(new Comparator[K] { + override def compare(a: K, b: K): Int = { + val h1 = if (a == null) 0 else a.hashCode() + val h2 = if (b == null) 0 else b.hashCode() + h1 - h2 + } + }) + + // A comparator for (Int, K) elements that orders them by partition and then possibly by key + private val partitionKeyComparator: Comparator[(Int, K)] = { + if (ordering.isDefined || aggregator.isDefined) { + // Sort by partition ID then key comparator + new Comparator[(Int, K)] { + override def compare(a: (Int, K), b: (Int, K)): Int = { + val partitionDiff = a._1 - b._1 + if (partitionDiff != 0) { + partitionDiff + } else { + keyComparator.compare(a._2, b._2) + } + } + } + } else { + // Just sort it by partition ID + new Comparator[(Int, K)] { + override def compare(a: (Int, K), b: (Int, K)): Int = { + a._1 - b._1 + } + } + } + } + + // Information about a spilled file. Includes sizes in bytes of "batches" written by the + // serializer as we periodically reset its stream, as well as number of elements in each + // partition, used to efficiently keep track of partitions when merging. + private[this] case class SpilledFile( + file: File, + blockId: BlockId, + serializerBatchSizes: Array[Long], + elementsPerPartition: Array[Long]) + private val spills = new ArrayBuffer[SpilledFile] + + def write(records: Iterator[_ <: Product2[K, V]]): Unit = { + // TODO: stop combining if we find that the reduction factor isn't high + val shouldCombine = aggregator.isDefined + + if (shouldCombine) { + // Combine values in-memory first using our AppendOnlyMap + val mergeValue = aggregator.get.mergeValue + val createCombiner = aggregator.get.createCombiner + var kv: Product2[K, V] = null + val update = (hadValue: Boolean, oldValue: C) => { + if (hadValue) mergeValue(oldValue, kv._2) else createCombiner(kv._2) + } + while (records.hasNext) { + elementsRead += 1 + kv = records.next() + map.changeValue((getPartition(kv._1), kv._1), update) + maybeSpill(usingMap = true) + } + } else { + // Stick values into our buffer + while (records.hasNext) { + elementsRead += 1 + val kv = records.next() + buffer.insert((getPartition(kv._1), kv._1), kv._2.asInstanceOf[C]) + maybeSpill(usingMap = false) + } + } + } + + private def maybeSpill(usingMap: Boolean): Unit = { + if (!spillingEnabled) { + return + } + + val collection: SizeTrackingPairCollection[(Int, K), C] = if (usingMap) map else buffer + + if (elementsRead > trackMemoryThreshold && elementsRead % 32 == 0 && --- End diff -- Maybe add a TODO here that says this is very similar to the EAOM code
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