SparK Shuffle之SortShffleWriter
SortShffleWriter的逻辑在write方法,查看代码
/** Write a bunch of records to this task's output */
override def write(records: Iterator[Product2[K, V]]): Unit = {
sorter = if (dep.mapSideCombine) {
require(dep.aggregator.isDefined, "Map-side combine without Aggregator specified!")
new ExternalSorter[K, V, C](
context, dep.aggregator, Some(dep.partitioner), dep.keyOrdering, dep.serializer)
} else {
// In this case we pass neither an aggregator nor an ordering to the sorter, because we don't
// care whether the keys get sorted in each partition; that will be done on the reduce side
// if the operation being run is sortByKey.
new ExternalSorter[K, V, V](
context, aggregator = None, Some(dep.partitioner), ordering = None, dep.serializer)
}
sorter.insertAll(records)
// Don't bother including the time to open the merged output file in the shuffle write time,
// because it just opens a single file, so is typically too fast to measure accurately
// (see SPARK-3570).
val output = shuffleBlockResolver.getDataFile(dep.shuffleId, mapId)
val tmp = Utils.tempFileWith(output)
try {
val blockId = ShuffleBlockId(dep.shuffleId, mapId, IndexShuffleBlockResolver.NOOP_REDUCE_ID)
val partitionLengths = sorter.writePartitionedFile(blockId, tmp)
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}")
}
}
}
在ExternalSorter的insertAll方法中,先判断是否需要进行聚合(Aggregation),如果需要,则根据键值进行合并(Combine),然后把这些数据写入到内存缓冲区中,如果排序中的Map占用的内存已经超越了使用的阈值,则将Map中的内容溢写到磁盘中,每一次溢写产生一个不同的文件。如果不需要聚合,则直接把数据写入到内存缓冲区中。
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) {
// 处理一个元素,就更新一次结果
addElementsRead()
kv = records.next()
map.changeValue((getPartition(kv._1), kv._1), update)
maybeSpillCollection(usingMap = true)
}
} else {
// Stick values into our buffer
while (records.hasNext) {
// 处理一个元素,就更新一次结果
addElementsRead()
val kv = records.next()
buffer.insert(getPartition(kv._1), kv._1, kv._2.asInstanceOf[C])
maybeSpillCollection(usingMap = false)
}
}
}
溢写到磁盘
1、如果需要map端的聚合:
估计map的大小,根据预估的map大小决定是否需要进行spill。如果需要spill,在spill之后,初始化一个新的PartitionedAppendOnlyMap。
2、如果不需要map端的聚合:
估计buffer的大小,根据预估的buffer大小决定是否需要进行spill。如果需要spill,spill之后,初始化一个新的PartitionedPairBuffer。
/**
* 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
}
}