Github user rxin commented on a diff in the pull request:
https://github.com/apache/spark/pull/1499#discussion_r15148079
--- Diff:
core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala ---
@@ -0,0 +1,390 @@
+/*
+ * 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 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
+ * @param serializer serializer to use
+ */
+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.getOrElse(null))
+ private val serInstance = ser.newInstance()
+
+ private val conf = SparkEnv.get.conf
+ 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 SizeTrackingBuffer[((Int, K), C)]
+
+ // Track how many elements we've read before we try to estimate memory.
Ideally we'd use
+ // map.size or buffer.size for this, but because users' Aggregators can
potentially increase
+ // the size of a merged element when we add values with the same key,
it's safer to track
+ // elements read from the input iterator.
+ private var elementsRead = 0L
+ 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",
0.3)
+ val safetyFraction = conf.getDouble("spark.shuffle.safetyFraction",
0.8)
+ (Runtime.getRuntime.maxMemory * memoryFraction * safetyFraction).toLong
+ }
+
+ // For now, just compare them by partition; later we can compare by key
as well
+ private val comparator = new Comparator[((Int, K), C)] {
+ override def compare(a: ((Int, K), C), b: ((Int, K), C)): Int = {
+ a._1._1 - b._1._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 case class SpilledFile(
+ file: File,
+ blockId: BlockId,
+ serializerBatchSizes: ArrayBuffer[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 += (((getPartition(kv._1), kv._1), kv._2.asInstanceOf[C]))
+ maybeSpill(usingMap = false)
+ }
+ }
+ }
+
+ private def maybeSpill(usingMap: Boolean): Unit = {
+ val collection: SizeTrackingCollection[((Int, K), C)] = if (usingMap)
map else buffer
+
+ if (elementsRead > trackMemoryThreshold && collection.atGrowThreshold)
{
+ // TODO: This is code from ExternalAppendOnlyMap that doesn't work
if there are two external
+ // collections being used in the same task. However we'll just copy
it for now.
+
+ val currentSize = collection.estimateSize()
+ var shouldSpill = false
+ val shuffleMemoryMap = SparkEnv.get.shuffleMemoryMap
+
+ // Atomically check whether there is sufficient memory in the global
pool for
+ // this map to grow and, if possible, allocate the required amount
+ shuffleMemoryMap.synchronized {
+ val threadId = Thread.currentThread().getId
+ val previouslyOccupiedMemory = shuffleMemoryMap.get(threadId)
+ val availableMemory = maxMemoryThreshold -
+ (shuffleMemoryMap.values.sum -
previouslyOccupiedMemory.getOrElse(0L))
+
+ // Assume map growth factor is 2x
+ shouldSpill = availableMemory < currentSize * 2
+ if (!shouldSpill) {
+ shuffleMemoryMap(threadId) = currentSize * 2
+ }
+ }
+ // Do not synchronize spills
+ if (shouldSpill) {
+ spill(currentSize, usingMap)
+ }
+ }
+ }
+
+ /**
+ * Spill the current in-memory collection to disk, adding a new file to
spills, and clear it.
+ *
+ * @param usingMap whether we're using a map or buffer as our current
in-memory collection
+ */
+ def spill(memorySize: Long, usingMap: Boolean): Unit = {
+ val collection: SizeTrackingCollection[((Int, K), C)] = if (usingMap)
map else buffer
+ val memorySize = collection.estimateSize()
+
+ spillCount += 1
+ logWarning("Spilling in-memory batch of %d MB to disk (%d spill%s so
far)"
+ .format(memorySize / (1024 * 1024), spillCount, if (spillCount > 1)
"s" else ""))
+ val (blockId, file) = diskBlockManager.createTempBlock()
+ var writer = blockManager.getDiskWriter(blockId, file, ser,
fileBufferSize)
+ var objectsWritten = 0
+
+ // List of batch sizes (bytes) in the order they are written to disk
+ val batchSizes = new ArrayBuffer[Long]
+
+ // How many elements we have in each partition
+ // TODO: this could become a sparser data structure
+ val elementsPerPartition = new Array[Long](numPartitions)
+
+ // Flush the disk writer's contents to disk, and update relevant
variables
+ def flush() = {
+ writer.commit()
+ val bytesWritten = writer.bytesWritten
+ batchSizes.append(bytesWritten)
+ _diskBytesSpilled += bytesWritten
+ objectsWritten = 0
+ }
+
+ try {
+ val it = collection.destructiveSortedIterator(comparator)
+ while (it.hasNext) {
+ val elem = it.next()
+ val partitionId = elem._1._1
+ val key = elem._1._2
+ val value = elem._2
+ writer.write(key)
+ writer.write(value)
+ elementsPerPartition(partitionId) += 1
+ objectsWritten += 1
+
+ if (objectsWritten == serializerBatchSize) {
+ flush()
+ writer.close()
+ writer = blockManager.getDiskWriter(blockId, file, ser,
fileBufferSize)
+ }
+ }
+ if (objectsWritten > 0) {
+ flush()
+ }
+ writer.close()
+ } catch {
+ case e: Exception =>
+ writer.close()
+ file.delete()
+ }
+
+ if (usingMap) {
+ map = new SizeTrackingAppendOnlyMap[(Int, K), C]
+ } else {
+ buffer = new SizeTrackingBuffer[((Int, K), C)]
+ }
+
+ spills.append(SpilledFile(file, blockId, batchSizes,
elementsPerPartition))
+ _memoryBytesSpilled += memorySize
+ }
+
+ /**
+ * Merge a sequence of sorted files, giving an iterator over partitions
and then over elements
+ * inside each partition. This can be used to either write out a new
file or return data to
+ * the user.
+ */
+ def merge(spills: Seq[SpilledFile], inMemory: Iterator[((Int, K), C)])
+ : Iterator[(Int, Iterator[Product2[K, C]])] = {
--- End diff --
I'd add @return to the javadoc to explain what the int represents.
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