ResultTask
概述
ResultTask 執行當前分區的計算,首先從ShuffleMapTask拿到當前partition的數據,會從所有的ShuffleMapTask都拿一遍當前的partition數據。最後合併所有的ResultTask輸出結果,返回給driver application。
成員變量
private[spark] class ResultTask[T, U](
stageId: Int,
stageAttemptId: Int,
taskBinary: Broadcast[Array[Byte]],
partition: Partition,
locs: Seq[TaskLocation],
val outputId: Int,
localProperties: Properties,
serializedTaskMetrics: Array[Byte],
jobId: Option[Int] = None,
appId: Option[String] = None,
appAttemptId: Option[String] = None,
isBarrier: Boolean = false)
extends Task[U](stageId, stageAttemptId, partition.index, localProperties, serializedTaskMetrics,
jobId, appId, appAttemptId, isBarrier)
runTask方法
override def runTask(context: TaskContext): U = {
// Deserialize the RDD and the func using the broadcast variables.
val threadMXBean = ManagementFactory.getThreadMXBean
val deserializeStartTime = System.currentTimeMillis()
val deserializeStartCpuTime = if (threadMXBean.isCurrentThreadCpuTimeSupported) {
threadMXBean.getCurrentThreadCpuTime
} else 0L
val ser = SparkEnv.get.closureSerializer.newInstance()
//反序列化ResultTask,結果爲rdd,和func函數
//taskBinary的值是在DAGScheduler.submitMissingTasks()方法中進行序列化的
val (rdd, func) = ser.deserialize[(RDD[T], (TaskContext, Iterator[T]) => U)](
ByteBuffer.wrap(taskBinary.value), Thread.currentThread.getContextClassLoader)
_executorDeserializeTime = System.currentTimeMillis() - deserializeStartTime
_executorDeserializeCpuTime = if (threadMXBean.isCurrentThreadCpuTimeSupported) {
threadMXBean.getCurrentThreadCpuTime - deserializeStartCpuTime
} else 0L
//整個ResultTask計算在 rdd.iterator(partition, context) 中完成
//此時的RDD爲:ShuffleRDD,所以rdd.iterator()方法調用的是ShuffleRDD.iterator()方法,會調用ShuffleRDD.compute()方法
//func 函數將Iterator轉換爲數組: RDD.collect()方法中的 (iter: Iterator[T]) => iter.toArray
func(context, rdd.iterator(partition, context))
}
ShuffleRDD.compute()方法
調用 BlockStoreShuffleReader.read()方法去各個節點拉取ShuffleMapTask輸出的當前partition的磁盤文件。
override def compute(split: Partition, context: TaskContext): Iterator[(K, C)] = {
val dep = dependencies.head.asInstanceOf[ShuffleDependency[K, V, C]]
SparkEnv.get.shuffleManager.getReader(dep.shuffleHandle, split.index, split.index + 1, context)
.read()
.asInstanceOf[Iterator[(K, C)]]
}
spark shuffle過程中典型的fetch shuffle file堆棧如下:
org.apache.spark.shuffle.IndexShuffleBlockResolver.getBlockData(IndexShuffleBlockResolver.scala:191)
at org.apache.spark.storage.BlockManager.getBlockData(BlockManager.scala:298)
at org.apache.spark.storage.ShuffleBlockFetcherIterator.fetchLocalBlocks(ShuffleBlockFetcherIterator.scala:238)
at org.apache.spark.storage.ShuffleBlockFetcherIterator.initialize(ShuffleBlockFetcherIterator.scala:269)
at org.apache.spark.storage.ShuffleBlockFetcherIterator.<init>(ShuffleBlockFetcherIterator.scala:112)
at org.apache.spark.shuffle.BlockStoreShuffleReader.read(BlockStoreShuffleReader.scala:43)
at org.apache.spark.rdd.ShuffledRDD.compute(ShuffledRDD.scala:98)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)