You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Sean Owen (JIRA)" <ji...@apache.org> on 2017/11/28 09:30:01 UTC
[jira] [Resolved] (SPARK-22593) submitMissingTask in DagScheduler
will call partitions function many times whch may be time consuming
[ https://issues.apache.org/jira/browse/SPARK-22593?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sean Owen resolved SPARK-22593.
-------------------------------
Resolution: Not A Problem
getPartitions is only called once. It cached a reference to this value.
Yes, it's only worth investigating changes where there's any plausible performance impact, ideally one that can be measured.
> submitMissingTask in DagScheduler will call partitions function many times whch may be time consuming
> -----------------------------------------------------------------------------------------------------
>
> Key: SPARK-22593
> URL: https://issues.apache.org/jira/browse/SPARK-22593
> Project: Spark
> Issue Type: Question
> Components: Spark Core
> Affects Versions: 2.2.0
> Reporter: tomzhu
> Priority: Minor
>
> when dagScheduler call submitMissing task, will create tasks and calling stage.rdd.partitions, it will can many times which may be time-consuming, the code is:
> {quote}
> val tasks: Seq[Task[_]] = try {
> val serializedTaskMetrics = closureSerializer.serialize(stage.latestInfo.taskMetrics).array()
> stage match {
> case stage: ShuffleMapStage =>
> stage.pendingPartitions.clear()
> partitionsToCompute.map { id =>
> val locs = taskIdToLocations(id)
> val part = stage.rdd.partitions(id)
> stage.pendingPartitions += id
> new ShuffleMapTask(stage.id, stage.latestInfo.attemptId,
> taskBinary, part, locs, properties, serializedTaskMetrics, Option(jobId),
> Option(sc.applicationId), sc.applicationAttemptId)
> }
> case stage: ResultStage =>
> partitionsToCompute.map { id =>
> val p: Int = stage.partitions(id)
> val part = stage.rdd.partitions(p) //here is a little time consuming.
> val locs = taskIdToLocations(id)
> new ResultTask(stage.id, stage.latestInfo.attemptId,
> taskBinary, part, locs, id, properties, serializedTaskMetrics,
> Option(jobId), Option(sc.applicationId), sc.applicationAttemptId)
> }
> }
> }
> {quote}
> for example, for a parallelCollectionRdd with 3 slices or partitions, to create task, the code will call stage.rdd.partitions three times, since stage.rdd.partitions will call getPartitions, so getPartions will call three times, it is a little time-cousuming. the stage.rdd.partitions code :
> {quote}
> final def partitions: Array[Partition] = {
> checkpointRDD.map(_.partitions).getOrElse {
> if (partitions_ == null) {
> partitions_ = getPartitions
> partitions_.zipWithIndex.foreach { case (partition, index) =>
> require(partition.index == index,
> s"partitions($index).partition == ${partition.index}, but it should equal $index")
> }
> }
> partitions_
> }
> }
> {quote}
> it would be better to avoid this.
--
This message was sent by Atlassian JIRA
(v6.4.14#64029)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org