Spark 源码分析 -- Task

Task是介于DAGScheduler和TaskScheduler中间的接口
在DAGScheduler, 需要把DAG中的每个stage的每个partitions封装成task
最终把taskset提交给TaskScheduler

 

/**
* A task to execute on a worker node.
*/
private[spark] abstract class Task[T](val stageId: Int) extends Serializable {
def run(attemptId: Long): T  //Task的核心函数
def preferredLocations: Seq[TaskLocation] = Nil //Spark关注locality,可以选择该task运行的location
var epoch: Long = -1   // Map output tracker epoch. Will be set by TaskScheduler.
var metrics: Option[TaskMetrics] = None
}

 

TaskContext

用于记录TaskMetrics和在Task中用到的callback

比如对于HadoopRDD, task完成时需要close input stream

package org.apache.spark

class TaskContext(
val stageId: Int,
val splitId: Int,
val attemptId: Long,
val runningLocally: Boolean = false,
val taskMetrics: TaskMetrics = TaskMetrics.empty() //TaskMetrics封装了task执行时一些指标和数据
) extends Serializable {

@transient val onCompleteCallbacks = new ArrayBuffer[() => Unit]

// Add a callback function to be executed on task completion. An example use
// is for HadoopRDD to register a callback to close the input stream.
def addOnCompleteCallback(f: () => Unit) {
onCompleteCallbacks += f
}

def executeOnCompleteCallbacks() {
onCompleteCallbacks.foreach{_()}
}
}

 

ResultTask

对应于Result Stage直接产生结果

package org.apache.spark.scheduler

private[spark] class ResultTask[T, U](
stageId: Int,
var rdd: RDD[T],
var func: (TaskContext, Iterator[T]) => U,
var partition: Int,
@transient locs: Seq[TaskLocation],
var outputId: Int)
extends Task(stageId) with Externalizable {

override def run(attemptId: Long): U = {  [u]// 对于resultTask, run就是返回执行的结果, 比如count值
val context = new TaskContext(stageId, partition, attemptId, runningLocally = false)
metrics = Some(context.taskMetrics)
try {
func(context, rdd.iterator(split, context)) // 直接就是对RDD的iterator调用func, 比如count函数
} finally {
context.executeOnCompleteCallbacks()
}
}
}

 

ShuffleMapTask

对应于ShuffleMap Stage, 产生的结果作为其他stage的输入

package org.apache.spark.scheduler

private[spark] class ShuffleMapTask(
stageId: Int,
var rdd: RDD[_],
var dep: ShuffleDependency[_,_],
var partition: Int,
@transient private var locs: Seq[TaskLocation])
extends Task[MapStatus](stageId)
with Externalizable
with Logging {

override def run(attemptId: Long): MapStatus = {
val numOutputSplits = dep.partitioner.numPartitions // 从ShuffleDependency的partitioner中获取到shuffle目标partition的个数

val taskContext = new TaskContext(stageId, partition, attemptId, runningLocally = false)
metrics = Some(taskContext.taskMetrics)

val blockManager = SparkEnv.get.blockManager // shuffle需要借助blockManager来完成
var shuffle: ShuffleBlocks = null
var buckets: ShuffleWriterGroup = null

try {
// Obtain all the block writers for shuffle blocks.
val ser = SparkEnv.get.serializerManager.get(dep.serializerClass)
shuffle = blockManager.shuffleBlockManager.forShuffle(dep.shuffleId, numOutputSplits, ser) // 创建shuffleBlockManager, 参数是shuffleId和目标partitions数目
buckets = shuffle.acquireWriters(partition) // 生成shuffle目标buckets(对应于partition)

// Write the map output to its associated buckets.
for (elem <- rdd.iterator(split, taskContext)) { // 从RDD中取出每个elem数据
val pair = elem.asInstanceOf[Product2[Any, Any]]
val bucketId = dep.partitioner.getPartition(pair._1) // 根据pair的key进行shuffle, 得到目标bucketid
buckets.writers(bucketId).write(pair) // 将pair数据写入bucket
}

// Commit这些buckets到block, 其他的RDD会从通过shuffleid找到这些block, 并读取数据
// Commit the writes. Get the size of each bucket block (total block size).
var totalBytes = 0L
val compressedSizes: Array[Byte] = buckets.writers.map { writer: BlockObjectWriter => // 计算所有buckets写入文件data的size总和(压缩值)
writer.commit()
writer.close()
val size = writer.size()
totalBytes += size
MapOutputTracker.compressSize(size)
}

// Update shuffle metrics.
val shuffleMetrics = new ShuffleWriteMetrics
shuffleMetrics.shuffleBytesWritten = totalBytes
metrics.get.shuffleWriteMetrics = Some(shuffleMetrics)

return new MapStatus(blockManager.blockManagerId, compressedSizes) // 返回值为MapStatus, 包含blockManagerId和写入的data size, 会被注册到MapOutputTracker
} catch { case e: Exception =>
// If there is an exception from running the task, revert the partial writes
// and throw the exception upstream to Spark.
if (buckets != null) {
buckets.writers.foreach(_.revertPartialWrites())
}
throw e
} finally {
// Release the writers back to the shuffle block manager.
if (shuffle != null && buckets != null) {
shuffle.releaseWriters(buckets)
}
// Execute the callbacks on task completion.
taskContext.executeOnCompleteCallbacks()
}
}

 

TaskSet

用于封装一个stage的所有的tasks, 以提交给TaskScheduler

package org.apache.spark.scheduler
/**
* A set of tasks submitted together to the low-level TaskScheduler, usually representing
* missing partitions of a particular stage.
*/
private[spark] class TaskSet(
val tasks: Array[Task[_]],
val stageId: Int,
val attempt: Int,
val priority: Int,
val properties: Properties) {
val id: String = stageId + "." + attempt

override def toString: String = "TaskSet " + id
}