External spilling - generalize batching logic
The existing implementation consists of a hack for Kryo specifically and only works for LZF compression. Introducing an intermediate batch-level stream takes care of pre-fetching and other arbitrary behavior of higher level streams in a more general way.
Author: Andrew Or <andrewor14@gmail.com>
== Merge branch commits ==
commit 3ddeb7ef89a0af2b685fb5d071aa0f71c975cc82
Author: Andrew Or <andrewor14@gmail.com>
Date: Wed Feb 5 12:09:32 2014 -0800
Also privatize fields
commit 090544a87a0767effd0c835a53952f72fc8d24f0
Author: Andrew Or <andrewor14@gmail.com>
Date: Wed Feb 5 10:58:23 2014 -0800
Privatize methods
commit 13920c918efe22e66a1760b14beceb17a61fd8cc
Author: Andrew Or <andrewor14@gmail.com>
Date: Tue Feb 4 16:34:15 2014 -0800
Update docs
commit bd5a1d7350467ed3dc19c2de9b2c9f531f0e6aa3
Author: Andrew Or <andrewor14@gmail.com>
Date: Tue Feb 4 13:44:24 2014 -0800
Typo: phyiscal -> physical
commit 287ef44e593ad72f7434b759be3170d9ee2723d2
Author: Andrew Or <andrewor14@gmail.com>
Date: Tue Feb 4 13:38:32 2014 -0800
Avoid reading the entire batch into memory; also simplify streaming logic
Additionally, address formatting comments.
commit 3df700509955f7074821e9aab1e74cb53c58b5a5
Merge: a531d2e 164489d
Author: Andrew Or <andrewor14@gmail.com>
Date: Mon Feb 3 18:27:49 2014 -0800
Merge branch 'master' of github.com:andrewor14/incubator-spark
commit a531d2e347acdcecf2d0ab72cd4f965ab5e145d8
Author: Andrew Or <andrewor14@gmail.com>
Date: Mon Feb 3 18:18:04 2014 -0800
Relax assumptions on compressors and serializers when batching
This commit introduces an intermediate layer of an input stream on the batch level.
This guards against interference from higher level streams (i.e. compression and
deserialization streams), especially pre-fetching, without specifically targeting
particular libraries (Kryo) and forcing shuffle spill compression to use LZF.
commit 164489d6f176bdecfa9dabec2dfce5504d1ee8af
Author: Andrew Or <andrewor14@gmail.com>
Date: Mon Feb 3 18:18:04 2014 -0800
Relax assumptions on compressors and serializers when batching
This commit introduces an intermediate layer of an input stream on the batch level.
This guards against interference from higher level streams (i.e. compression and
deserialization streams), especially pre-fetching, without specifically targeting
particular libraries (Kryo) and forcing shuffle spill compression to use LZF.
This commit is contained in:
Andrew Or
Patrick Wendell
parent
0b448df6ac
commit
1896c6e7c9
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@ -66,6 +66,11 @@ private[spark] abstract class BlockObjectWriter(val blockId: BlockId) {
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* Cumulative time spent performing blocking writes, in ns.
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*/
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def timeWriting(): Long
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/**
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* Number of bytes written so far
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*/
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def bytesWritten: Long
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}
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/** BlockObjectWriter which writes directly to a file on disk. Appends to the given file. */
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@ -183,7 +188,8 @@ private[spark] class DiskBlockObjectWriter(
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// Only valid if called after close()
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override def timeWriting() = _timeWriting
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def bytesWritten: Long = {
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// Only valid if called after commit()
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override def bytesWritten: Long = {
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lastValidPosition - initialPosition
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}
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}
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@ -50,7 +50,7 @@ private[spark] class DiskBlockManager(shuffleManager: ShuffleBlockManager, rootD
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addShutdownHook()
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/**
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* Returns the phyiscal file segment in which the given BlockId is located.
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* Returns the physical file segment in which the given BlockId is located.
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* If the BlockId has been mapped to a specific FileSegment, that will be returned.
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* Otherwise, we assume the Block is mapped to a whole file identified by the BlockId directly.
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*/
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@ -24,11 +24,11 @@ import scala.collection.mutable
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import scala.collection.mutable.ArrayBuffer
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import it.unimi.dsi.fastutil.io.FastBufferedInputStream
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import com.google.common.io.ByteStreams
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import org.apache.spark.{Logging, SparkEnv}
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import org.apache.spark.io.LZFCompressionCodec
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import org.apache.spark.serializer.{KryoDeserializationStream, Serializer}
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import org.apache.spark.storage.{BlockId, BlockManager, DiskBlockObjectWriter}
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import org.apache.spark.serializer.Serializer
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import org.apache.spark.storage.{BlockId, BlockManager}
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/**
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* An append-only map that spills sorted content to disk when there is insufficient space for it
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@ -84,12 +84,15 @@ private[spark] class ExternalAppendOnlyMap[K, V, C](
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// Number of in-memory pairs inserted before tracking the map's shuffle memory usage
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private val trackMemoryThreshold = 1000
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// Size of object batches when reading/writing from serializers. Objects are written in
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// batches, with each batch using its own serialization stream. This cuts down on the size
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// of reference-tracking maps constructed when deserializing a stream.
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//
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// NOTE: Setting this too low can cause excess copying when serializing, since some serializers
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// grow internal data structures by growing + copying every time the number of objects doubles.
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/**
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* Size of object batches when reading/writing from serializers.
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*
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* Objects are written in batches, with each batch using its own serialization stream. This
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* cuts down on the size of reference-tracking maps constructed when deserializing a stream.
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*
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* NOTE: Setting this too low can cause excessive copying when serializing, since some serializers
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* grow internal data structures by growing + copying every time the number of objects doubles.
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*/
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private val serializerBatchSize = sparkConf.getLong("spark.shuffle.spill.batchSize", 10000)
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// How many times we have spilled so far
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@ -100,7 +103,6 @@ private[spark] class ExternalAppendOnlyMap[K, V, C](
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private var _diskBytesSpilled = 0L
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private val fileBufferSize = sparkConf.getInt("spark.shuffle.file.buffer.kb", 100) * 1024
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private val syncWrites = sparkConf.getBoolean("spark.shuffle.sync", false)
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private val comparator = new KCComparator[K, C]
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private val ser = serializer.newInstance()
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@ -153,37 +155,21 @@ private[spark] class ExternalAppendOnlyMap[K, V, C](
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logWarning("Spilling in-memory map of %d MB to disk (%d time%s so far)"
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.format(mapSize / (1024 * 1024), spillCount, if (spillCount > 1) "s" else ""))
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val (blockId, file) = diskBlockManager.createTempBlock()
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var writer = blockManager.getDiskWriter(blockId, file, serializer, fileBufferSize)
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var objectsWritten = 0
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/* IMPORTANT NOTE: To avoid having to keep large object graphs in memory, this approach
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* closes and re-opens serialization and compression streams within each file. This makes some
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* assumptions about the way that serialization and compression streams work, specifically:
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*
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* 1) The serializer input streams do not pre-fetch data from the underlying stream.
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*
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* 2) Several compression streams can be opened, written to, and flushed on the write path
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* while only one compression input stream is created on the read path
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*
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* In practice (1) is only true for Java, so we add a special fix below to make it work for
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* Kryo. (2) is only true for LZF and not Snappy, so we coerce this to use LZF.
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*
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* To avoid making these assumptions we should create an intermediate stream that batches
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* objects and sends an EOF to the higher layer streams to make sure they never prefetch data.
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* This is a bit tricky because, within each segment, you'd need to track the total number
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* of bytes written and then re-wind and write it at the beginning of the segment. This will
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* most likely require using the file channel API.
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*/
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// List of batch sizes (bytes) in the order they are written to disk
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val batchSizes = new ArrayBuffer[Long]
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val shouldCompress = blockManager.shouldCompress(blockId)
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val compressionCodec = new LZFCompressionCodec(sparkConf)
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def wrapForCompression(outputStream: OutputStream) = {
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if (shouldCompress) compressionCodec.compressedOutputStream(outputStream) else outputStream
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// Flush the disk writer's contents to disk, and update relevant variables
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def flush() = {
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writer.commit()
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val bytesWritten = writer.bytesWritten
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batchSizes.append(bytesWritten)
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_diskBytesSpilled += bytesWritten
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objectsWritten = 0
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}
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def getNewWriter = new DiskBlockObjectWriter(blockId, file, serializer, fileBufferSize,
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wrapForCompression, syncWrites)
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var writer = getNewWriter
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var objectsWritten = 0
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try {
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val it = currentMap.destructiveSortedIterator(comparator)
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while (it.hasNext) {
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@ -192,22 +178,21 @@ private[spark] class ExternalAppendOnlyMap[K, V, C](
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objectsWritten += 1
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if (objectsWritten == serializerBatchSize) {
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writer.commit()
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flush()
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writer.close()
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_diskBytesSpilled += writer.bytesWritten
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writer = getNewWriter
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objectsWritten = 0
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writer = blockManager.getDiskWriter(blockId, file, serializer, fileBufferSize)
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}
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}
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if (objectsWritten > 0) writer.commit()
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if (objectsWritten > 0) {
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flush()
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}
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} finally {
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// Partial failures cannot be tolerated; do not revert partial writes
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writer.close()
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_diskBytesSpilled += writer.bytesWritten
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}
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currentMap = new SizeTrackingAppendOnlyMap[K, C]
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spilledMaps.append(new DiskMapIterator(file, blockId))
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spilledMaps.append(new DiskMapIterator(file, blockId, batchSizes))
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// Reset the amount of shuffle memory used by this map in the global pool
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val shuffleMemoryMap = SparkEnv.get.shuffleMemoryMap
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@ -239,12 +224,12 @@ private[spark] class ExternalAppendOnlyMap[K, V, C](
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private class ExternalIterator extends Iterator[(K, C)] {
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// A fixed-size queue that maintains a buffer for each stream we are currently merging
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val mergeHeap = new mutable.PriorityQueue[StreamBuffer]
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private val mergeHeap = new mutable.PriorityQueue[StreamBuffer]
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// Input streams are derived both from the in-memory map and spilled maps on disk
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// The in-memory map is sorted in place, while the spilled maps are already in sorted order
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val sortedMap = currentMap.destructiveSortedIterator(comparator)
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val inputStreams = Seq(sortedMap) ++ spilledMaps
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private val sortedMap = currentMap.destructiveSortedIterator(comparator)
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private val inputStreams = Seq(sortedMap) ++ spilledMaps
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inputStreams.foreach { it =>
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val kcPairs = getMorePairs(it)
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@ -252,11 +237,12 @@ private[spark] class ExternalAppendOnlyMap[K, V, C](
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}
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/**
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* Fetch from the given iterator until a key of different hash is retrieved. In the
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* event of key hash collisions, this ensures no pairs are hidden from being merged.
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* Fetch from the given iterator until a key of different hash is retrieved.
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*
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* In the event of key hash collisions, this ensures no pairs are hidden from being merged.
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* Assume the given iterator is in sorted order.
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*/
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def getMorePairs(it: Iterator[(K, C)]): ArrayBuffer[(K, C)] = {
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private def getMorePairs(it: Iterator[(K, C)]): ArrayBuffer[(K, C)] = {
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val kcPairs = new ArrayBuffer[(K, C)]
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if (it.hasNext) {
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var kc = it.next()
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@ -274,7 +260,7 @@ private[spark] class ExternalAppendOnlyMap[K, V, C](
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* If the given buffer contains a value for the given key, merge that value into
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* baseCombiner and remove the corresponding (K, C) pair from the buffer
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*/
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def mergeIfKeyExists(key: K, baseCombiner: C, buffer: StreamBuffer): C = {
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private def mergeIfKeyExists(key: K, baseCombiner: C, buffer: StreamBuffer): C = {
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var i = 0
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while (i < buffer.pairs.size) {
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val (k, c) = buffer.pairs(i)
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@ -293,7 +279,8 @@ private[spark] class ExternalAppendOnlyMap[K, V, C](
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override def hasNext: Boolean = mergeHeap.exists(!_.pairs.isEmpty)
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/**
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* Select a key with the minimum hash, then combine all values with the same key from all input streams.
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* Select a key with the minimum hash, then combine all values with the same key from all
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* input streams
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*/
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override def next(): (K, C) = {
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// Select a key from the StreamBuffer that holds the lowest key hash
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*
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* StreamBuffers are ordered by the minimum key hash found across all of their own pairs.
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*/
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case class StreamBuffer(iterator: Iterator[(K, C)], pairs: ArrayBuffer[(K, C)])
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private case class StreamBuffer(iterator: Iterator[(K, C)], pairs: ArrayBuffer[(K, C)])
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extends Comparable[StreamBuffer] {
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def minKeyHash: Int = {
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@ -355,52 +342,54 @@ private[spark] class ExternalAppendOnlyMap[K, V, C](
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/**
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* An iterator that returns (K, C) pairs in sorted order from an on-disk map
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*/
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private class DiskMapIterator(file: File, blockId: BlockId) extends Iterator[(K, C)] {
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val fileStream = new FileInputStream(file)
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val bufferedStream = new FastBufferedInputStream(fileStream, fileBufferSize)
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private class DiskMapIterator(file: File, blockId: BlockId, batchSizes: ArrayBuffer[Long])
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extends Iterator[(K, C)] {
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private val fileStream = new FileInputStream(file)
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private val bufferedStream = new FastBufferedInputStream(fileStream, fileBufferSize)
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val shouldCompress = blockManager.shouldCompress(blockId)
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val compressionCodec = new LZFCompressionCodec(sparkConf)
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val compressedStream =
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if (shouldCompress) {
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compressionCodec.compressedInputStream(bufferedStream)
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// An intermediate stream that reads from exactly one batch
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// This guards against pre-fetching and other arbitrary behavior of higher level streams
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private var batchStream = nextBatchStream()
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private var compressedStream = blockManager.wrapForCompression(blockId, batchStream)
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private var deserializeStream = ser.deserializeStream(compressedStream)
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private var nextItem: (K, C) = null
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private var objectsRead = 0
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/**
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* Construct a stream that reads only from the next batch
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*/
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private def nextBatchStream(): InputStream = {
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if (batchSizes.length > 0) {
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ByteStreams.limit(bufferedStream, batchSizes.remove(0))
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} else {
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// No more batches left
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bufferedStream
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}
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var deserializeStream = ser.deserializeStream(compressedStream)
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var objectsRead = 0
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var nextItem: (K, C) = null
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var eof = false
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def readNextItem(): (K, C) = {
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if (!eof) {
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try {
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if (objectsRead == serializerBatchSize) {
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val newInputStream = deserializeStream match {
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case stream: KryoDeserializationStream =>
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// Kryo's serializer stores an internal buffer that pre-fetches from the underlying
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// stream. We need to capture this buffer and feed it to the new serialization
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// stream so that the bytes are not lost.
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val kryoInput = stream.input
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val remainingBytes = kryoInput.limit() - kryoInput.position()
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val extraBuf = kryoInput.readBytes(remainingBytes)
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new SequenceInputStream(new ByteArrayInputStream(extraBuf), compressedStream)
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case _ => compressedStream
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}
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deserializeStream = ser.deserializeStream(newInputStream)
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/**
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* Return the next (K, C) pair from the deserialization stream.
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*
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* If the current batch is drained, construct a stream for the next batch and read from it.
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* If no more pairs are left, return null.
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*/
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private def readNextItem(): (K, C) = {
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try {
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val item = deserializeStream.readObject().asInstanceOf[(K, C)]
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objectsRead += 1
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if (objectsRead == serializerBatchSize) {
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batchStream = nextBatchStream()
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compressedStream = blockManager.wrapForCompression(blockId, batchStream)
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deserializeStream = ser.deserializeStream(compressedStream)
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objectsRead = 0
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}
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objectsRead += 1
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return deserializeStream.readObject().asInstanceOf[(K, C)]
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item
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} catch {
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case e: EOFException =>
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eof = true
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cleanup()
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}
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}
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null
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}
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}
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override def hasNext: Boolean = {
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if (nextItem == null) {
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@ -419,7 +408,7 @@ private[spark] class ExternalAppendOnlyMap[K, V, C](
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}
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// TODO: Ensure this gets called even if the iterator isn't drained.
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def cleanup() {
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private def cleanup() {
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deserializeStream.close()
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file.delete()
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}
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@ -158,9 +158,7 @@ Apart from these, the following properties are also available, and may be useful
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<td>spark.shuffle.spill.compress</td>
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<td>true</td>
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<td>
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Whether to compress data spilled during shuffles. If enabled, spill compression
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always uses the `org.apache.spark.io.LZFCompressionCodec` codec,
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regardless of the value of `spark.io.compression.codec`.
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Whether to compress data spilled during shuffles.
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</td>
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</tr>
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<tr>
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Loading…
Reference in a new issue