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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.
*/
package org.apache.spark.util.collection
import java.io._
import java.util.Comparator
import scala.collection.mutable
import scala.collection.mutable.ArrayBuffer
import com.google.common.io.ByteStreams
import org.apache.spark._
import org.apache.spark.executor.ShuffleWriteMetrics
import org.apache.spark.internal.Logging
import org.apache.spark.serializer._
import org.apache.spark.storage.{BlockId, DiskBlockObjectWriter}
/**
* 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.
*
* Note: Although ExternalSorter is a fairly generic sorter, some of its configuration is tied
* to its use in sort-based shuffle (for example, its block compression is controlled by
* `spark.shuffle.compress`). We may need to revisit this if ExternalSorter is used in other
* non-shuffle contexts where we might want to use different configuration settings.
*
* @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.
*
* Users interact with this class in the following way:
*
* 1. Instantiate an ExternalSorter.
*
* 2. Call insertAll() with a set of records.
*
* 3. Request an iterator() back to traverse sorted/aggregated records.
* - or -
* Invoke writePartitionedFile() to create a file containing sorted/aggregated outputs
* that can be used in Spark's sort shuffle.
*
* At a high level, this class works internally as follows:
*
* - We repeatedly fill up buffers of in-memory data, using either a PartitionedAppendOnlyMap if
* we want to combine by key, or a PartitionedPairBuffer if we don't.
* Inside these buffers, we sort elements by partition ID and then possibly also by key.
* To avoid calling the partitioner multiple times with each key, we store the partition ID
* alongside each record.
*
* - 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 or file output, 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](
context: TaskContext,
aggregator: Option[Aggregator[K, V, C]] = None,
partitioner: Option[Partitioner] = None,
ordering: Option[Ordering[K]] = None,
serializer: Serializer = SparkEnv.get.serializer)
extends Spillable[WritablePartitionedPairCollection[K, C]](context.taskMemoryManager())
with Logging {
private val conf = SparkEnv.get.conf
private val numPartitions = partitioner.map(_.numPartitions).getOrElse(1)
private val shouldPartition = numPartitions > 1
private def getPartition(key: K): Int = {
if (shouldPartition) partitioner.get.getPartition(key) else 0
}
private val blockManager = SparkEnv.get.blockManager
private val diskBlockManager = blockManager.diskBlockManager
private val serializerManager = SparkEnv.get.serializerManager
private val serInstance = serializer.newInstance()
// Use getSizeAsKb (not bytes) to maintain backwards compatibility if no units are provided
private val fileBufferSize = conf.getSizeAsKb("spark.shuffle.file.buffer", "32k").toInt * 1024
// Size of object batches when reading/writing from serializers.
//
// Objects are written in batches, with each batch using its own serialization stream. This
// cuts down on the size of reference-tracking maps constructed when deserializing a stream.
//
// NOTE: Setting this too low can cause excessive copying when serializing, since some serializers
// grow internal data structures by growing + copying every time the number of objects doubles.
private val serializerBatchSize = conf.getLong("spark.shuffle.spill.batchSize", 10000)
// 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.
@volatile private var map = new PartitionedAppendOnlyMap[K, C]
@volatile private var buffer = new PartitionedPairBuffer[K, C]
// Total spilling statistics
private var _diskBytesSpilled = 0L
def diskBytesSpilled: Long = _diskBytesSpilled
// Peak size of the in-memory data structure observed so far, in bytes
private var _peakMemoryUsedBytes: Long = 0L
def peakMemoryUsedBytes: Long = _peakMemoryUsedBytes
@volatile private var isShuffleSort: Boolean = true
private val forceSpillFiles = new ArrayBuffer[SpilledFile]
@volatile private var readingIterator: SpillableIterator = null
// A comparator for keys K that orders them within a partition to allow aggregation or sorting.
// 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 and no ordering are 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()
if (h1 < h2) -1 else if (h1 == h2) 0 else 1
}
})
private def comparator: Option[Comparator[K]] = {
if (ordering.isDefined || aggregator.isDefined) {
Some(keyComparator)
} else {
None
}
}
// 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]
/**
* Number of files this sorter has spilled so far.
* Exposed for testing.
*/
private[spark] def numSpills: Int = spills.size
def insertAll(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) {
addElementsRead()
kv = records.next()
map.changeValue((getPartition(kv._1), kv._1), update)
maybeSpillCollection(usingMap = true)
}
} else {
// Stick values into our buffer
while (records.hasNext) {
addElementsRead()
val kv = records.next()
buffer.insert(getPartition(kv._1), kv._1, kv._2.asInstanceOf[C])
maybeSpillCollection(usingMap = false)
}
}
}
/**
* Spill the current in-memory collection to disk if needed.
*
* @param usingMap whether we're using a map or buffer as our current in-memory collection
*/
private def maybeSpillCollection(usingMap: Boolean): Unit = {
var estimatedSize = 0L
if (usingMap) {
estimatedSize = map.estimateSize()
if (maybeSpill(map, estimatedSize)) {
map = new PartitionedAppendOnlyMap[K, C]
}
} else {
estimatedSize = buffer.estimateSize()
if (maybeSpill(buffer, estimatedSize)) {
buffer = new PartitionedPairBuffer[K, C]
}
}
if (estimatedSize > _peakMemoryUsedBytes) {
_peakMemoryUsedBytes = estimatedSize
}
}
/**
* Spill our in-memory collection to a sorted file that we can merge later.
* We add this file into `spilledFiles` to find it later.
*
* @param collection whichever collection we're using (map or buffer)
*/
override protected[this] def spill(collection: WritablePartitionedPairCollection[K, C]): Unit = {
val inMemoryIterator = collection.destructiveSortedWritablePartitionedIterator(comparator)
val spillFile = spillMemoryIteratorToDisk(inMemoryIterator)
spills += spillFile
}
/**
* Force to spilling the current in-memory collection to disk to release memory,
* It will be called by TaskMemoryManager when there is not enough memory for the task.
*/
override protected[this] def forceSpill(): Boolean = {
if (isShuffleSort) {
false
} else {
assert(readingIterator != null)
val isSpilled = readingIterator.spill()
if (isSpilled) {
map = null
buffer = null
}
isSpilled
}
}
/**
* Spill contents of in-memory iterator to a temporary file on disk.
*/
private[this] def spillMemoryIteratorToDisk(inMemoryIterator: WritablePartitionedIterator)
: SpilledFile = {
// Because these files may be read during shuffle, their compression must be controlled by
// spark.shuffle.compress instead of spark.shuffle.spill.compress, so we need to use
// createTempShuffleBlock here; see SPARK-3426 for more context.
val (blockId, file) = diskBlockManager.createTempShuffleBlock()
// These variables are reset after each flush
var objectsWritten: Long = 0
val spillMetrics: ShuffleWriteMetrics = new ShuffleWriteMetrics
val writer: DiskBlockObjectWriter =
blockManager.getDiskWriter(blockId, file, serInstance, fileBufferSize, spillMetrics)
// 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
val elementsPerPartition = new Array[Long](numPartitions)
// Flush the disk writer's contents to disk, and update relevant variables.
// The writer is committed at the end of this process.
def flush(): Unit = {
val segment = writer.commitAndGet()
batchSizes += segment.length
_diskBytesSpilled += segment.length
objectsWritten = 0
}
var success = false
try {
while (inMemoryIterator.hasNext) {
val partitionId = inMemoryIterator.nextPartition()
require(partitionId >= 0 && partitionId < numPartitions,
s"partition Id: ${partitionId} should be in the range [0, ${numPartitions})")
inMemoryIterator.writeNext(writer)
elementsPerPartition(partitionId) += 1
objectsWritten += 1
if (objectsWritten == serializerBatchSize) {
flush()
}
}
if (objectsWritten > 0) {
flush()
} else {
writer.revertPartialWritesAndClose()
}
success = true
} finally {
if (success) {
writer.close()
} else {
// This code path only happens if an exception was thrown above before we set success;
// close our stuff and let the exception be thrown further
writer.revertPartialWritesAndClose()
if (file.exists()) {
if (!file.delete()) {
logWarning(s"Error deleting ${file}")
}
}
}
}
SpilledFile(file, blockId, batchSizes.toArray, elementsPerPartition)
}
/**
* 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.
*
* Returns an iterator over all the data written to this object, grouped by partition. For each
* partition we then have an iterator over its contents, and these are expected to be accessed
* in order (you can't "skip ahead" to one partition without reading the previous one).
* Guaranteed to return a key-value pair for each partition, in order of partition ID.
*/
private def merge(spills: Seq[SpilledFile], inMemory: Iterator[((Int, K), C)])
: Iterator[(Int, Iterator[Product2[K, C]])] = {
val readers = spills.map(new SpillReader(_))
val inMemBuffered = inMemory.buffered
(0 until numPartitions).iterator.map { p =>
val inMemIterator = new IteratorForPartition(p, inMemBuffered)
val iterators = readers.map(_.readNextPartition()) ++ Seq(inMemIterator)
if (aggregator.isDefined) {
// Perform partial aggregation across partitions
(p, mergeWithAggregation(
iterators, aggregator.get.mergeCombiners, keyComparator, ordering.isDefined))
} else if (ordering.isDefined) {
// No aggregator given, but we have an ordering (e.g. used by reduce tasks in sortByKey);
// sort the elements without trying to merge them
(p, mergeSort(iterators, ordering.get))
} else {
(p, iterators.iterator.flatten)
}
}
}
/**
* Merge-sort a sequence of (K, C) iterators using a given a comparator for the keys.
*/
private def mergeSort(iterators: Seq[Iterator[Product2[K, C]]], comparator: Comparator[K])
: Iterator[Product2[K, C]] =
{
val bufferedIters = iterators.filter(_.hasNext).map(_.buffered)
type Iter = BufferedIterator[Product2[K, C]]
val heap = new mutable.PriorityQueue[Iter]()(new Ordering[Iter] {
// Use the reverse order because PriorityQueue dequeues the max
override def compare(x: Iter, y: Iter): Int = comparator.compare(y.head._1, x.head._1)
})
heap.enqueue(bufferedIters: _*) // Will contain only the iterators with hasNext = true
new Iterator[Product2[K, C]] {
override def hasNext: Boolean = !heap.isEmpty
override def next(): Product2[K, C] = {
if (!hasNext) {
throw new NoSuchElementException
}
val firstBuf = heap.dequeue()
val firstPair = firstBuf.next()
if (firstBuf.hasNext) {
heap.enqueue(firstBuf)
}
firstPair
}
}
}
/**
* Merge a sequence of (K, C) iterators by aggregating values for each key, assuming that each
* iterator is sorted by key with a given comparator. If the comparator is not a total ordering
* (e.g. when we sort objects by hash code and different keys may compare as equal although
* they're not), we still merge them by doing equality tests for all keys that compare as equal.
*/
private def mergeWithAggregation(
iterators: Seq[Iterator[Product2[K, C]]],
mergeCombiners: (C, C) => C,
comparator: Comparator[K],
totalOrder: Boolean)
: Iterator[Product2[K, C]] =
{
if (!totalOrder) {
// We only have a partial ordering, e.g. comparing the keys by hash code, which means that
// multiple distinct keys might be treated as equal by the ordering. To deal with this, we
// need to read all keys considered equal by the ordering at once and compare them.
new Iterator[Iterator[Product2[K, C]]] {
val sorted = mergeSort(iterators, comparator).buffered
// Buffers reused across elements to decrease memory allocation
val keys = new ArrayBuffer[K]
val combiners = new ArrayBuffer[C]
override def hasNext: Boolean = sorted.hasNext
override def next(): Iterator[Product2[K, C]] = {
if (!hasNext) {
throw new NoSuchElementException
}
keys.clear()
combiners.clear()
val firstPair = sorted.next()
keys += firstPair._1
combiners += firstPair._2
val key = firstPair._1
while (sorted.hasNext && comparator.compare(sorted.head._1, key) == 0) {
val pair = sorted.next()
var i = 0
var foundKey = false
while (i < keys.size && !foundKey) {
if (keys(i) == pair._1) {
combiners(i) = mergeCombiners(combiners(i), pair._2)
foundKey = true
}
i += 1
}
if (!foundKey) {
keys += pair._1
combiners += pair._2
}
}
// Note that we return an iterator of elements since we could've had many keys marked
// equal by the partial order; we flatten this below to get a flat iterator of (K, C).
keys.iterator.zip(combiners.iterator)
}
}.flatMap(i => i)
} else {
// We have a total ordering, so the objects with the same key are sequential.
new Iterator[Product2[K, C]] {
val sorted = mergeSort(iterators, comparator).buffered
override def hasNext: Boolean = sorted.hasNext
override def next(): Product2[K, C] = {
if (!hasNext) {
throw new NoSuchElementException
}
val elem = sorted.next()
val k = elem._1
var c = elem._2
while (sorted.hasNext && sorted.head._1 == k) {
val pair = sorted.next()
c = mergeCombiners(c, pair._2)
}
(k, c)
}
}
}
}
/**
* An internal class for reading a spilled file partition by partition. Expects all the
* partitions to be requested in order.
*/
private[this] class SpillReader(spill: SpilledFile) {
// Serializer batch offsets; size will be batchSize.length + 1
val batchOffsets = spill.serializerBatchSizes.scanLeft(0L)(_ + _)
// Track which partition and which batch stream we're in. These will be the indices of
// the next element we will read. We'll also store the last partition read so that
// readNextPartition() can figure out what partition that was from.
var partitionId = 0
var indexInPartition = 0L
var batchId = 0
var indexInBatch = 0
var lastPartitionId = 0
skipToNextPartition()
// Intermediate file and deserializer streams that read from exactly one batch
// This guards against pre-fetching and other arbitrary behavior of higher level streams
var fileStream: FileInputStream = null
var deserializeStream = nextBatchStream() // Also sets fileStream
var nextItem: (K, C) = null
var finished = false
/** Construct a stream that only reads from the next batch */
def nextBatchStream(): DeserializationStream = {
// Note that batchOffsets.length = numBatches + 1 since we did a scan above; check whether
// we're still in a valid batch.
if (batchId < batchOffsets.length - 1) {
if (deserializeStream != null) {
deserializeStream.close()
fileStream.close()
deserializeStream = null
fileStream = null
}
val start = batchOffsets(batchId)
fileStream = new FileInputStream(spill.file)
fileStream.getChannel.position(start)
batchId += 1
val end = batchOffsets(batchId)
assert(end >= start, "start = " + start + ", end = " + end +
", batchOffsets = " + batchOffsets.mkString("[", ", ", "]"))
val bufferedStream = new BufferedInputStream(ByteStreams.limit(fileStream, end - start))
val wrappedStream = serializerManager.wrapStream(spill.blockId, bufferedStream)
serInstance.deserializeStream(wrappedStream)
} else {
// No more batches left
cleanup()
null
}
}
/**
* Update partitionId if we have reached the end of our current partition, possibly skipping
* empty partitions on the way.
*/
private def skipToNextPartition() {
while (partitionId < numPartitions &&
indexInPartition == spill.elementsPerPartition(partitionId)) {
partitionId += 1
indexInPartition = 0L
}
}
/**
* Return the next (K, C) pair from the deserialization stream and update partitionId,
* indexInPartition, indexInBatch and such to match its location.
*
* If the current batch is drained, construct a stream for the next batch and read from it.
* If no more pairs are left, return null.
*/
private def readNextItem(): (K, C) = {
if (finished || deserializeStream == null) {
return null
}
val k = deserializeStream.readKey().asInstanceOf[K]
val c = deserializeStream.readValue().asInstanceOf[C]
lastPartitionId = partitionId
// Start reading the next batch if we're done with this one
indexInBatch += 1
if (indexInBatch == serializerBatchSize) {
indexInBatch = 0
deserializeStream = nextBatchStream()
}
// Update the partition location of the element we're reading
indexInPartition += 1
skipToNextPartition()
// If we've finished reading the last partition, remember that we're done
if (partitionId == numPartitions) {
finished = true
if (deserializeStream != null) {
deserializeStream.close()
}
}
(k, c)
}
var nextPartitionToRead = 0
def readNextPartition(): Iterator[Product2[K, C]] = new Iterator[Product2[K, C]] {
val myPartition = nextPartitionToRead
nextPartitionToRead += 1
override def hasNext: Boolean = {
if (nextItem == null) {
nextItem = readNextItem()
if (nextItem == null) {
return false
}
}
assert(lastPartitionId >= myPartition)
// Check that we're still in the right partition; note that readNextItem will have returned
// null at EOF above so we would've returned false there
lastPartitionId == myPartition
}
override def next(): Product2[K, C] = {
if (!hasNext) {
throw new NoSuchElementException
}
val item = nextItem
nextItem = null
item
}
}
// Clean up our open streams and put us in a state where we can't read any more data
def cleanup() {
batchId = batchOffsets.length // Prevent reading any other batch
val ds = deserializeStream
deserializeStream = null
fileStream = null
if (ds != null) {
ds.close()
}
// NOTE: We don't do file.delete() here because that is done in ExternalSorter.stop().
// This should also be fixed in ExternalAppendOnlyMap.
}
}
/**
* Returns a destructive iterator for iterating over the entries of this map.
* If this iterator is forced spill to disk to release memory when there is not enough memory,
* it returns pairs from an on-disk map.
*/
def destructiveIterator(memoryIterator: Iterator[((Int, K), C)]): Iterator[((Int, K), C)] = {
if (isShuffleSort) {
memoryIterator
} else {
readingIterator = new SpillableIterator(memoryIterator)
readingIterator
}
}
/**
* Return an iterator over all the data written to this object, grouped by partition and
* aggregated by the requested aggregator. For each partition we then have an iterator over its
* contents, and these are expected to be accessed in order (you can't "skip ahead" to one
* partition without reading the previous one). Guaranteed to return a key-value pair for each
* partition, in order of partition ID.
*
* For now, we just merge all the spilled files in once pass, but this can be modified to
* support hierarchical merging.
* Exposed for testing.
*/
def partitionedIterator: Iterator[(Int, Iterator[Product2[K, C]])] = {
val usingMap = aggregator.isDefined
val collection: WritablePartitionedPairCollection[K, C] = if (usingMap) map else buffer
if (spills.isEmpty) {
// Special case: if we have only in-memory data, we don't need to merge streams, and perhaps
// we don't even need to sort by anything other than partition ID
if (!ordering.isDefined) {
// The user hasn't requested sorted keys, so only sort by partition ID, not key
groupByPartition(destructiveIterator(collection.partitionedDestructiveSortedIterator(None)))
} else {
// We do need to sort by both partition ID and key
groupByPartition(destructiveIterator(
collection.partitionedDestructiveSortedIterator(Some(keyComparator))))
}
} else {
// Merge spilled and in-memory data
merge(spills, destructiveIterator(
collection.partitionedDestructiveSortedIterator(comparator)))
}
}
/**
* Return an iterator over all the data written to this object, aggregated by our aggregator.
*/
def iterator: Iterator[Product2[K, C]] = {
isShuffleSort = false
partitionedIterator.flatMap(pair => pair._2)
}
/**
* Write all the data added into this ExternalSorter into a file in the disk store. This is
* called by the SortShuffleWriter.
*
* @param blockId block ID to write to. The index file will be blockId.name + ".index".
* @return array of lengths, in bytes, of each partition of the file (used by map output tracker)
*/
def writePartitionedFile(
blockId: BlockId,
outputFile: File): Array[Long] = {
// Track location of each range in the output file
val lengths = new Array[Long](numPartitions)
val writer = blockManager.getDiskWriter(blockId, outputFile, serInstance, fileBufferSize,
context.taskMetrics().shuffleWriteMetrics)
if (spills.isEmpty) {
// Case where we only have in-memory data
val collection = if (aggregator.isDefined) map else buffer
val it = collection.destructiveSortedWritablePartitionedIterator(comparator)
while (it.hasNext) {
val partitionId = it.nextPartition()
while (it.hasNext && it.nextPartition() == partitionId) {
it.writeNext(writer)
}
val segment = writer.commitAndGet()
lengths(partitionId) = segment.length
}
} else {
// We must perform merge-sort; get an iterator by partition and write everything directly.
for ((id, elements) <- this.partitionedIterator) {
if (elements.hasNext) {
for (elem <- elements) {
writer.write(elem._1, elem._2)
}
val segment = writer.commitAndGet()
lengths(id) = segment.length
}
}
}
writer.close()
context.taskMetrics().incMemoryBytesSpilled(memoryBytesSpilled)
context.taskMetrics().incDiskBytesSpilled(diskBytesSpilled)
context.taskMetrics().incPeakExecutionMemory(peakMemoryUsedBytes)
lengths
}
def stop(): Unit = {
spills.foreach(s => s.file.delete())
spills.clear()
forceSpillFiles.foreach(s => s.file.delete())
forceSpillFiles.clear()
if (map != null || buffer != null || readingIterator != null) {
map = null // So that the memory can be garbage-collected
buffer = null // So that the memory can be garbage-collected
readingIterator = null // So that the memory can be garbage-collected
releaseMemory()
}
}
/**
* Given a stream of ((partition, key), combiner) pairs *assumed to be sorted by partition ID*,
* group together the pairs for each partition into a sub-iterator.
*
* @param data an iterator of elements, assumed to already be sorted by partition ID
*/
private def groupByPartition(data: Iterator[((Int, K), C)])
: Iterator[(Int, Iterator[Product2[K, C]])] =
{
val buffered = data.buffered
(0 until numPartitions).iterator.map(p => (p, new IteratorForPartition(p, buffered)))
}
/**
* An iterator that reads only the elements for a given partition ID from an underlying buffered
* stream, assuming this partition is the next one to be read. Used to make it easier to return
* partitioned iterators from our in-memory collection.
*/
private[this] class IteratorForPartition(partitionId: Int, data: BufferedIterator[((Int, K), C)])
extends Iterator[Product2[K, C]]
{
override def hasNext: Boolean = data.hasNext && data.head._1._1 == partitionId
override def next(): Product2[K, C] = {
if (!hasNext) {
throw new NoSuchElementException
}
val elem = data.next()
(elem._1._2, elem._2)
}
}
private[this] class SpillableIterator(var upstream: Iterator[((Int, K), C)])
extends Iterator[((Int, K), C)] {
private val SPILL_LOCK = new Object()
private var nextUpstream: Iterator[((Int, K), C)] = null
private var cur: ((Int, K), C) = readNext()
private var hasSpilled: Boolean = false
def spill(): Boolean = SPILL_LOCK.synchronized {
if (hasSpilled) {
false
} else {
val inMemoryIterator = new WritablePartitionedIterator {
private[this] var cur = if (upstream.hasNext) upstream.next() else null
def writeNext(writer: DiskBlockObjectWriter): Unit = {
writer.write(cur._1._2, cur._2)
cur = if (upstream.hasNext) upstream.next() else null
}
def hasNext(): Boolean = cur != null
def nextPartition(): Int = cur._1._1
}
logInfo(s"Task ${context.taskAttemptId} force spilling in-memory map to disk and " +
s" it will release ${org.apache.spark.util.Utils.bytesToString(getUsed())} memory")
val spillFile = spillMemoryIteratorToDisk(inMemoryIterator)
forceSpillFiles += spillFile
val spillReader = new SpillReader(spillFile)
nextUpstream = (0 until numPartitions).iterator.flatMap { p =>
val iterator = spillReader.readNextPartition()
iterator.map(cur => ((p, cur._1), cur._2))
}
hasSpilled = true
true
}
}
def readNext(): ((Int, K), C) = SPILL_LOCK.synchronized {
if (nextUpstream != null) {
upstream = nextUpstream
nextUpstream = null
}
if (upstream.hasNext) {
upstream.next()
} else {
null
}
}
override def hasNext(): Boolean = cur != null
override def next(): ((Int, K), C) = {
val r = cur
cur = readNext()
r
}
}
}
