diff options
4 files changed, 64 insertions, 13 deletions
diff --git a/core/src/main/scala/org/apache/spark/Aggregator.scala b/core/src/main/scala/org/apache/spark/Aggregator.scala index cda3a95c84..6d439fdc68 100644 --- a/core/src/main/scala/org/apache/spark/Aggregator.scala +++ b/core/src/main/scala/org/apache/spark/Aggregator.scala @@ -32,7 +32,7 @@ case class Aggregator[K, V, C] ( mergeCombiners: (C, C) => C) { private val sparkConf = SparkEnv.get.conf - private val externalSorting = sparkConf.getBoolean("spark.shuffle.externalSorting", true) + private val externalSorting = sparkConf.getBoolean("spark.shuffle.spill", true) def combineValuesByKey(iter: Iterator[_ <: Product2[K, V]], context: TaskContext) : Iterator[(K, C)] = { diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala index 6f1345c57a..6461deee32 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala @@ -80,6 +80,8 @@ private[spark] class BlockManager( val compressShuffle = conf.getBoolean("spark.shuffle.compress", true) // Whether to compress RDD partitions that are stored serialized val compressRdds = conf.getBoolean("spark.rdd.compress", false) + // Whether to compress shuffle output temporarily spilled to disk + val compressShuffleSpill = conf.getBoolean("spark.shuffle.spill.compress", false) val heartBeatFrequency = BlockManager.getHeartBeatFrequency(conf) @@ -790,6 +792,7 @@ private[spark] class BlockManager( case ShuffleBlockId(_, _, _) => compressShuffle case BroadcastBlockId(_) => compressBroadcast case RDDBlockId(_, _) => compressRdds + case TempBlockId(_) => compressShuffleSpill case _ => false } diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala index c63f47cc45..64e9b436f0 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala @@ -26,8 +26,8 @@ import scala.collection.mutable import scala.collection.mutable.ArrayBuffer import org.apache.spark.{Logging, SparkEnv} -import org.apache.spark.serializer.Serializer -import org.apache.spark.storage.{DiskBlockManager, DiskBlockObjectWriter} +import org.apache.spark.serializer.{KryoDeserializationStream, KryoSerializationStream, Serializer} +import org.apache.spark.storage.{BlockId, BlockManager, DiskBlockManager, DiskBlockObjectWriter} /** * An append-only map that spills sorted content to disk when there is insufficient space for it @@ -60,7 +60,7 @@ private[spark] class ExternalAppendOnlyMap[K, V, C]( mergeValue: (C, V) => C, mergeCombiners: (C, C) => C, serializer: Serializer = SparkEnv.get.serializerManager.default, - diskBlockManager: DiskBlockManager = SparkEnv.get.blockManager.diskBlockManager) + blockManager: BlockManager = SparkEnv.get.blockManager) extends Iterable[(K, C)] with Serializable with Logging { import ExternalAppendOnlyMap._ @@ -68,6 +68,7 @@ private[spark] class ExternalAppendOnlyMap[K, V, C]( private var currentMap = new SizeTrackingAppendOnlyMap[K, C] private val spilledMaps = new ArrayBuffer[DiskMapIterator] private val sparkConf = SparkEnv.get.conf + private val diskBlockManager = blockManager.diskBlockManager // Collective memory threshold shared across all running tasks private val maxMemoryThreshold = { @@ -82,6 +83,14 @@ private[spark] class ExternalAppendOnlyMap[K, V, C]( // Number of in-memory pairs inserted before tracking the map's shuffle memory usage private val trackMemoryThreshold = 1000 + // 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 excess copying when serializing, since some serializers + // grow internal data structures by growing + copying every time the number of objects doubles. + private val serializerBatchSize = sparkConf.getLong("spark.shuffle.spill.batchSize", 10000) + // How many times we have spilled so far private var spillCount = 0 @@ -143,22 +152,35 @@ private[spark] class ExternalAppendOnlyMap[K, V, C]( logWarning("Spilling in-memory map of %d MB to disk (%d time%s so far)" .format(mapSize / (1024 * 1024), spillCount, if (spillCount > 1) "s" else "")) val (blockId, file) = diskBlockManager.createTempBlock() - val writer = - new DiskBlockObjectWriter(blockId, file, serializer, fileBufferSize, identity, syncWrites) + + val compressStream: OutputStream => OutputStream = blockManager.wrapForCompression(blockId, _) + def getNewWriter = new DiskBlockObjectWriter(blockId, file, serializer, fileBufferSize, + compressStream, syncWrites) + + var writer = getNewWriter + var objectsWritten = 0 try { val it = currentMap.destructiveSortedIterator(comparator) while (it.hasNext) { val kv = it.next() writer.write(kv) + objectsWritten += 1 + + if (objectsWritten == serializerBatchSize) { + writer.commit() + writer = getNewWriter + objectsWritten = 0 + } } - writer.commit() + + if (objectsWritten > 0) writer.commit() } finally { // Partial failures cannot be tolerated; do not revert partial writes _diskBytesSpilled += writer.bytesWritten writer.close() } currentMap = new SizeTrackingAppendOnlyMap[K, C] - spilledMaps.append(new DiskMapIterator(file)) + spilledMaps.append(new DiskMapIterator(file, blockId)) // Reset the amount of shuffle memory used by this map in the global pool val shuffleMemoryMap = SparkEnv.get.shuffleMemoryMap @@ -306,16 +328,35 @@ private[spark] class ExternalAppendOnlyMap[K, V, C]( /** * An iterator that returns (K, C) pairs in sorted order from an on-disk map */ - private class DiskMapIterator(file: File) extends Iterator[(K, C)] { + private class DiskMapIterator(file: File, blockId: BlockId) extends Iterator[(K, C)] { val fileStream = new FileInputStream(file) - val bufferedStream = new FastBufferedInputStream(fileStream) - val deserializeStream = ser.deserializeStream(bufferedStream) + val bufferedStream = new FastBufferedInputStream(fileStream, fileBufferSize) + val compressedStream = blockManager.wrapForCompression(blockId, bufferedStream) + var deserializeStream = ser.deserializeStream(compressedStream) + var objectsRead = 0 + var nextItem: (K, C) = null var eof = false def readNextItem(): (K, C) = { if (!eof) { try { + if (objectsRead == serializerBatchSize) { + val newInputStream = deserializeStream match { + case stream: KryoDeserializationStream => + // Kryo's serializer stores an internal buffer that pre-fetches from the underlying + // stream. We need to capture this buffer and feed it to the new serialization + // stream so that the bytes are not lost. + val kryoInput = stream.input + val remainingBytes = kryoInput.limit() - kryoInput.position() + val extraBuf = kryoInput.readBytes(remainingBytes) + new SequenceInputStream(new ByteArrayInputStream(extraBuf), compressedStream) + case _ => compressedStream + } + deserializeStream = ser.deserializeStream(newInputStream) + objectsRead = 0 + } + objectsRead += 1 return deserializeStream.readObject().asInstanceOf[(K, C)] } catch { case e: EOFException => diff --git a/docs/configuration.md b/docs/configuration.md index 40a57c4bc6..be06bd19be 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -116,7 +116,7 @@ Apart from these, the following properties are also available, and may be useful <td>0.3</td> <td> Fraction of Java heap to use for aggregation and cogroups during shuffles, if - <code>spark.shuffle.externalSorting</code> is enabled. At any given time, the collective size of + <code>spark.shuffle.spill</code> is true. At any given time, the collective size of all in-memory maps used for shuffles is bounded by this limit, beyond which the contents will begin to spill to disk. If spills are often, consider increasing this value at the expense of <code>spark.storage.memoryFraction</code>. @@ -155,6 +155,13 @@ Apart from these, the following properties are also available, and may be useful </td> </tr> <tr> + <td>spark.shuffle.spill.compress</td> + <td>false</td> + <td> + Whether to compress data spilled during shuffles. + </td> +</tr> +<tr> <td>spark.broadcast.compress</td> <td>true</td> <td> @@ -388,7 +395,7 @@ Apart from these, the following properties are also available, and may be useful </td> </tr> <tr> - <td>spark.shuffle.externalSorting</td> + <td>spark.shuffle.spill</td> <td>true</td> <td> If set to "true", limits the amount of memory used during reduces by spilling data out to disk. This spilling |