shufflemanager的实现类：sortshufflemanager
Spark 0.8及以前 Hash Based Shuffle
在Shuffle Write过程按照Hash的方式重组Partition的数据，不进行排序。每个map端的任务为每个reduce端的Task生成一个文件，通常会产生大量的文件（即对应为M*R个中间文件，其中M表示map端的Task个数，R表示reduce端的Task个数），伴随大量的随机磁盘IO操作与大量的内存开销。
Shuffle Read过程如果有combiner操作，那么它会把拉到的数据保存在一个Spark封装的哈希表（AppendOnlyMap）中进行合并。在代码结构上：
- org.apache.spark.storage.ShuffleBlockManager负责Shuffle Write
- org.apache.spark.BlockStoreShuffleFetcher负责Shuffle Read
- org.apache.spark.Aggregator负责combine，依赖于AppendOnlyMap
Spark 0.8.1 为Hash Based Shuffle引入File Consolidation机制
通过文件合并，中间文件的生成方式修改为每个执行单位（一个Executor中的执行单位等于Core的个数除以每个Task所需的Core数）为每个reduce端的任务生成一个文件。最终可以将文件个数从MR修改为EC/T*R，其中，E表示Executor的个数，C表示每个Executor中可用Core的个数，T表示Task所分配的Core的个数。是否采用Consolidate机制，需要配置spark.shuffle.consolidateFiles参数
Spark 0.9 引入ExternalAppendOnlyMap
在combine的时候，可以将数据spill到磁盘，然后通过堆排序merge
Spark 1.1 引入Sort Based Shuffle，但默认仍为Hash Based Shuffle
在Sort Based Shuffle的Shuffle Write阶段，map端的任务会按照Partition id以及key对记录进行排序。同时将全部结果写到一个数据文件中，同时生成一个索引文件，reduce端的Task可以通过该索引文件获取相关的数据。
在代码结构上：
从以前的ShuffleBlockManager中分离出ShuffleManager来专门管理Shuffle Writer和Shuffle Reader。两种Shuffle方式分别对应
org.apache.spark.shuffle.hash.HashShuffleManager和org.apache.spark.shuffle.sort.SortShuffleManager，可通过spark.shuffle.manager参数配置。两种Shuffle方式有各自的ShuffleWriter：
org.apache.spark.shuffle.hash.HashShuffle和org.apache.spark.shuffle.sort.SortShuffleWriter；但共用一个ShuffleReader，即org.apache.spark.shuffle.hash.HashShuffleReader。
org.apache.spark.util.collection.ExternalSorter实现排序功能。可通过对spark.shuffle.spill参数配置，决定是否可以在排序时将临时数据Spill到磁盘。
Spark 1.2 默认的Shuffle方式改为Sort Based Shuffle
Spark 1.4 引入Tungsten-Sort Based Shuffle
将数据记录用序列化的二进制方式存储，把排序转化成指针数组的排序，引入堆外内存空间和新的内存管理模型，这些技术决定了使用Tungsten-Sort要符合一些严格的限制，比如Shuffle dependency不能带有aggregation、输出不能排序等。由于堆外内存的管理基于JDK Sun Unsafe API，故Tungsten-Sort Based Shuffle也被称为Unsafe Shuffle。
在代码层面：
- 新增org.apache.spark.shuffle.unsafe.UnsafeShuffleManager
- 新增org.apache.spark.shuffle.unsafe.UnsafeShuffleWriter(用java实现)
- ShuffleReader复用HashShuffleReader
Spark 1.6 Tungsten-sort并入Sort Based Shuffle
由SortShuffleManager自动判断选择最佳Shuffle方式，如果检测到满足Tungsten-sort条件会自动采用Tungsten-sort Based Shuffle，否则采用Sort Based Shuffle。
在代码方面：
- UnsafeShuffleManager合并到SortShuffleManager
- HashShuffleReader 重命名为BlockStoreShuffleReader，Sort Based Shuffle和Hash Based Shuffle仍共用ShuffleReader。
Spark 2.0 Hash Based Shuffle退出历史舞台，从此Spark只有Sort Based Shuffle，ShuffleManager的实现类就只有SortShufflemanager
1、SortShufflemanager详解
SortShufflemanager.getWriter:
override def getWriter[K, V](
handle: ShuffleHandle,
mapId: Int,
context: TaskContext): ShuffleWriter[K, V] = {
numMapsForShuffle.putIfAbsent(
handle.shuffleId, handle.asInstanceOf[BaseShuffleHandle[_, _, _]].numMaps)
val env = SparkEnv.get
handle match {
case unsafeShuffleHandle: SerializedShuffleHandle[K @unchecked, V @unchecked] =>
new UnsafeShuffleWriter(
env.blockManager,
shuffleBlockResolver.asInstanceOf[IndexShuffleBlockResolver],
context.taskMemoryManager(),
unsafeShuffleHandle,
mapId,
context,
env.conf)
case bypassMergeSortHandle: BypassMergeSortShuffleHandle[K @unchecked, V @unchecked] =>
new BypassMergeSortShuffleWriter(
env.blockManager,
shuffleBlockResolver.asInstanceOf[IndexShuffleBlockResolver],
bypassMergeSortHandle,
mapId,
context,
env.conf)
case other: BaseShuffleHandle[K @unchecked, V @unchecked, _] =>
new SortShuffleWriter(shuffleBlockResolver, other, mapId, context)
}
}
SortShufflemanager.getReader:
override def getReader[K, C](
handle: ShuffleHandle,
startPartition: Int,
endPartition: Int,
context: TaskContext): ShuffleReader[K, C] = {
new BlockStoreShuffleReader(
handle.asInstanceOf[BaseShuffleHandle[K, _, C]], startPartition, endPartition, context)
}
2、BypassMergeSortShuffleWriter
BypassMergeSortShuffleWriter类似于hash shuffle，但是将output file合并成一个文件
1）、BypassMergeSortShuffleWriter.write
public void write(Iterator<Product2<K, V>> records) throws IOException {
//如果record为空
if (!records.hasNext()) {
partitionLengths = new long[numPartitions];
shuffleBlockResolver.writeIndexFileAndCommit(shuffleId, mapId, partitionLengths, null);
mapStatus = MapStatus$.MODULE$.apply(blockManager.shuffleServerId(), partitionLengths);
return;
}
final SerializerInstance serInstance = serializer.newInstance();
final long openStartTime = System.nanoTime();
//获取partition写入磁盘文件的writer
partitionWriters = new DiskBlockObjectWriter[numPartitions];
for (int i = 0; i < numPartitions; i++) {
final Tuple2<TempShuffleBlockId, File> tempShuffleBlockIdPlusFile =
blockManager.diskBlockManager().createTempShuffleBlock();
final File file = tempShuffleBlockIdPlusFile._2();
final BlockId blockId = tempShuffleBlockIdPlusFile._1();
partitionWriters[i] =
blockManager.getDiskWriter(blockId, file, serInstance, fileBufferSize, writeMetrics);
}
//根据partitioner将记录写入磁盘
while (records.hasNext()) {
final Product2<K, V> record = records.next();
final K key = record._1();
partitionWriters[partitioner.getPartition(key)].write(key, record._2());
}
//获取每个ShuffleBlock，ShuffleBlock被称为FileSegment
partitionWriterSegments = new FileSegment[numPartitions];
for (int i = 0; i < numPartitions; i++) {
final DiskBlockObjectWriter writer = partitionWriters[i];
partitionWriterSegments[i] = writer.commitAndGet();
writer.close();
}
//合并文件以及写index文件
File output = shuffleBlockResolver.getDataFile(shuffleId, mapId);
File tmp = Utils.tempFileWith(output);
try {
partitionLengths = writePartitionedFile(tmp);
shuffleBlockResolver.writeIndexFileAndCommit(shuffleId, mapId, partitionLengths, tmp);
} finally {
if (tmp.exists() && !tmp.delete()) {
logger.error("Error while deleting temp file {}", tmp.getAbsolutePath());
}
}
mapStatus = MapStatus$.MODULE$.apply(blockManager.shuffleServerId(), partitionLengths);
}
2）、BypassMergeSortShuffleWriter.writePartitionedFile
上面代码中的writePartitionedFile作用是合并文件 File outputFile
private long[] writePartitionedFile(File outputFile) throws IOException {
//合并文件
final FileOutputStream out = new FileOutputStream(outputFile, true);
final long writeStartTime = System.nanoTime();
boolean threwException = true;
try {
for (int i = 0; i < numPartitions; i++) {
final File file = partitionWriterSegments[i].file();
if (file.exists()) {
final FileInputStream in = new FileInputStream(file);
boolean copyThrewException = true;
try {
lengths[i] = Utils.copyStream(in, out, false, transferToEnabled);
copyThrewException = false;
} finally {
Closeables.close(in, copyThrewException);
}
if (!file.delete()) {
logger.error("Unable to delete file for partition {}", i);
}
}
}
threwException = false;
} finally {
Closeables.close(out, threwException);
writeMetrics.incWriteTime(System.nanoTime() - writeStartTime);
}
partitionWriters = null;
//返回文件的偏移量
return lengths;
}
3、SortShuffleWriter
1）、SortShuffleWriter.writer
override def write(records: Iterator[Product2[K, V]]): Unit = {
sorter = if (dep.mapSideCombine) {
new ExternalSorter[K, V, C](
context, dep.aggregator, Some(dep.partitioner), dep.keyOrdering, dep.serializer)
} else {
//在这种情况下，我们既不传递聚合器，也不传递排序器，因为我们不关心键在每个分区中是否被排序;如果运行的操作是sortByKey，那么将在reduce方面进行。
new ExternalSorter[K, V, V](
context, aggregator = None, Some(dep.partitioner), ordering = None, dep.serializer)
}
//ExternalSorter将记录排序（数据排序后放在Execution内存中或者spill到磁盘上）
sorter.insertAll(records)
//将内存中以及磁盘中的文件合并
val output = shuffleBlockResolver.getDataFile(dep.shuffleId, mapId)
//通过工具类创建临时文件
val tmp = Utils.tempFileWith(output)
try {
val blockId = ShuffleBlockId(dep.shuffleId, mapId, IndexShuffleBlockResolver.NOOP_REDUCE_ID)
//将buffer或者map中的数据写入文件，各个partition
val partitionLengths = sorter.writePartitionedFile(blockId, tmp)
//写index文件
shuffleBlockResolver.writeIndexFileAndCommit(dep.shuffleId, mapId, partitionLengths, tmp)
mapStatus = MapStatus(blockManager.shuffleServerId, partitionLengths)
} finally {
if (tmp.exists() && !tmp.delete()) {
logError(s"Error while deleting temp file ${tmp.getAbsolutePath}")
}
}
}
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