org.apache.spark.scheduler.ActiveJob.scala Maven / Gradle / Ivy
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* The ASF licenses this file to You under the Apache License, Version 2.0
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*
* http://www.apache.org/licenses/LICENSE-2.0
*
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package org.apache.spark.scheduler
import java.util.Properties
import org.apache.spark.util.CallSite
/**
* A running job in the DAGScheduler. Jobs can be of two types: a result job, which computes a
* ResultStage to execute an action, or a map-stage job, which computes the map outputs for a
* ShuffleMapStage before any downstream stages are submitted. The latter is used for adaptive
* query planning, to look at map output statistics before submitting later stages. We distinguish
* between these two types of jobs using the finalStage field of this class.
*
* Jobs are only tracked for "leaf" stages that clients directly submitted, through DAGScheduler's
* submitJob or submitMapStage methods. However, either type of job may cause the execution of
* other earlier stages (for RDDs in the DAG it depends on), and multiple jobs may share some of
* these previous stages. These dependencies are managed inside DAGScheduler.
*
* @param jobId A unique ID for this job.
* @param finalStage The stage that this job computes (either a ResultStage for an action or a
* ShuffleMapStage for submitMapStage).
* @param callSite Where this job was initiated in the user's program (shown on UI).
* @param listener A listener to notify if tasks in this job finish or the job fails.
* @param properties Scheduling properties attached to the job, such as fair scheduler pool name.
*/
private[spark] class ActiveJob(
val jobId: Int,
val finalStage: Stage,
val callSite: CallSite,
val listener: JobListener,
val properties: Properties) {
/**
* Number of partitions we need to compute for this job. Note that result stages may not need
* to compute all partitions in their target RDD, for actions like first() and lookup().
*/
val numPartitions = finalStage match {
case r: ResultStage => r.partitions.length
case m: ShuffleMapStage => m.rdd.partitions.length
}
/** Which partitions of the stage have finished */
val finished = Array.fill[Boolean](numPartitions)(false)
var numFinished = 0
/** Resets the status of all partitions in this stage so they are marked as not finished. */
def resetAllPartitions(): Unit = {
(0 until numPartitions).foreach(finished.update(_, false))
numFinished = 0
}
}