Github user zhzhan commented on a diff in the pull request:

    https://github.com/apache/spark/pull/15541#discussion_r84002353
  
    --- Diff: core/src/main/scala/org/apache/spark/scheduler/TaskAssigner.scala 
---
    @@ -0,0 +1,233 @@
    +/*
    + * Licensed to the Apache Software Foundation (ASF) under one or more
    + * contributor license agreements.  See the NOTICE file distributed with
    + * this work for additional information regarding copyright ownership.
    + * The ASF licenses this file to You under the Apache License, Version 2.0
    + * (the "License"); you may not use this file except in compliance with
    + * the License.  You may obtain a copy of the License at
    + *
    + *    http://www.apache.org/licenses/LICENSE-2.0
    + *
    + * Unless required by applicable law or agreed to in writing, software
    + * distributed under the License is distributed on an "AS IS" BASIS,
    + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    + * See the License for the specific language governing permissions and
    + * limitations under the License.
    + */
    +
    +package org.apache.spark.scheduler
    +
    +import scala.collection.mutable.ArrayBuffer
    +import scala.collection.mutable.PriorityQueue
    +import scala.util.Random
    +
    +import org.apache.spark.internal.{config, Logging}
    +import org.apache.spark.SparkConf
    +import org.apache.spark.util.Utils
    +
    +/** Tracking the current state of the workers with available cores and 
assigned task list. */
    +class OfferState(val workOffer: WorkerOffer) {
    +  // The current remaining cores that can be allocated to tasks.
    +  var coresAvailable: Int = workOffer.cores
    +  // The list of tasks that are assigned to this worker.
    +  val tasks = new ArrayBuffer[TaskDescription](coresAvailable)
    +}
    +
    +/**
    + * TaskAssigner is the base class for all task assigner implementations, 
and can be
    + * extended to implement different task scheduling algorithms.
    + * Together with [[org.apache.spark.scheduler.TaskScheduler 
TaskScheduler]], TaskAssigner
    + * is used to assign tasks to workers with available cores. Internally, 
TaskScheduler, requested
    + * to perform task assignment given available workers, first sorts the 
candidate tasksets,
    + * and then for each taskset, it takes a number of rounds to request 
TaskAssigner for task
    + * assignment with different the locality restrictions until there is 
either no qualified
    + * workers or no valid tasks to be assigned.
    + *
    + * TaskAssigner is responsible to maintain the worker availability state 
and task assignment
    + * information. The contract between 
[[org.apache.spark.scheduler.TaskScheduler TaskScheduler]]
    + * and TaskAssigner is as follows. First, TaskScheduler invokes 
construct() of TaskAssigner to
    + * initialize the its internal worker states at the beginning of resource 
offering. Before each
    + * round of task assignment for a taskset, TaskScheduler invoke the init() 
of TaskAssigner to
    + * initialize the data structure for the round. When performing real task 
assignment,
    + * hasNext()/getNext() is used by TaskScheduler to check the worker 
availability and retrieve
    + * current offering from TaskAssigner. Then offerAccepted is used by 
TaskScheduler to notify
    + * the TaskAssigner so that TaskAssigner can decide whether the current 
offer is valid or not for
    + * the next request. After task assignment is done, TaskScheduler invokes 
the tasks() to
    + * retrieve all the task assignment information, and eventually, invokes 
reset() method so that
    + * TaskAssigner can cleanup its internal maintained resources.
    + */
    +
    +private[scheduler] abstract class TaskAssigner {
    +  var offer: Seq[OfferState] = _
    +  var CPUS_PER_TASK = 1
    +
    +  def withCpuPerTask(CPUS_PER_TASK: Int): Unit = {
    +    this.CPUS_PER_TASK = CPUS_PER_TASK
    +  }
    +
    +  // The final assigned offer returned to TaskScheduler.
    +  final def tasks: Seq[ArrayBuffer[TaskDescription]] = offer.map(_.tasks)
    +
    +  // Invoked at the beginning of resource offering to construct the offer 
with the workoffers.
    +  def construct(workOffer: Seq[WorkerOffer]): Unit = {
    +    offer = workOffer.map(o => new OfferState(o))
    +  }
    +
    +  // Invoked at each round of Taskset assignment to initialize the 
internal structure.
    +  def init(): Unit
    +
    +  // Whether there is offer available to be used inside of one round of 
Taskset assignment.
    +  def hasNext: Boolean
    +
    +  // Returned the next assigned offer based on the task assignment 
strategy.
    +  def getNext(): OfferState
    +
    +  // Invoked by the TaskScheduler to indicate whether the current offer is 
accepted or not so that
    +  // the assigner can decide whether the current worker is valid for the 
next offering.
    +  def offerAccepted(assigned: Boolean): Unit
    +
    +  // Invoked at the end of resource offering to release internally 
maintained resources.
    +  // Subclass is responsible to release its own private resources.
    +  def reset(): Unit = {
    +    offer = null
    +  }
    +}
    +
    +object TaskAssigner extends Logging {
    +  private val roundrobin = classOf[RoundRobinAssigner].getCanonicalName
    +  private val packed = classOf[PackedAssigner].getCanonicalName
    +  private val balanced = classOf[BalancedAssigner].getCanonicalName
    +  private val assignerMap: Map[String, String] =
    +    Map("roundrobin" -> roundrobin,
    +      "packed" -> packed,
    +      "balanced" -> balanced)
    +
    +  def init(conf: SparkConf): TaskAssigner = {
    +    val assignerName = conf.get(config.SPARK_SCHEDULER_TASK_ASSIGNER.key, 
"roundrobin")
    +      .toLowerCase()
    +    val className = assignerMap.getOrElse(assignerName, roundrobin)
    +    val CPUS_PER_TASK = conf.getInt("spark.task.cpus", 1)
    +    val assigner = try {
    +      logInfo(s"Constructing an assigner as $className")
    +      Utils.classForName(className).getConstructor()
    +        .newInstance().asInstanceOf[TaskAssigner]
    +    } catch {
    +      case _: Throwable =>
    +        logInfo(s"$assignerName cannot be constructed, fallback to default 
$roundrobin.")
    +        new RoundRobinAssigner()
    +    }
    +    assigner.withCpuPerTask(CPUS_PER_TASK)
    +    assigner
    +  }
    +}
    +
    +/**
    + * Assign the task to workers with available cores in roundrobin manner.
    + */
    +class RoundRobinAssigner extends TaskAssigner {
    +  private var idx = 0
    +
    +  override def construct(workOffer: Seq[WorkerOffer]): Unit = {
    +    offer = Random.shuffle(workOffer.map(o => new OfferState(o)))
    +  }
    +
    +  override def init(): Unit = {
    +    idx = 0
    +  }
    +
    +  override def hasNext: Boolean = idx < offer.size
    +
    +  override def getNext(): OfferState = {
    +    offer(idx)
    +  }
    +
    +  override def offerAccepted(assigned: Boolean): Unit = {
    +    idx += 1
    +  }
    +
    +  override def reset(): Unit = {
    +    super.reset
    +    idx = 0
    +  }
    +}
    +
    +/**
    + * Assign the task to workers with the most available cores.
    + */
    +class BalancedAssigner extends TaskAssigner {
    +  private var maxHeap: PriorityQueue[OfferState] = _
    +  private var currentOffer: OfferState = _
    +
    +  override def construct(workOffer: Seq[WorkerOffer]): Unit = {
    +    offer = Random.shuffle(workOffer.map(o => new OfferState(o)))
    +  }
    +
    +  implicit val ord: Ordering[OfferState] = new Ordering[OfferState] {
    +    def compare(x: OfferState, y: OfferState): Int = {
    +      return Ordering[Int].compare(x.coresAvailable, y.coresAvailable)
    +    }
    +  }
    +
    +  override def init(): Unit = {
    +    maxHeap = new PriorityQueue[OfferState]()
    +    offer.filter(_.coresAvailable >= 
CPUS_PER_TASK).foreach(maxHeap.enqueue(_))
    +  }
    +
    +  override def hasNext: Boolean = maxHeap.nonEmpty
    +
    +  override def getNext(): OfferState = {
    +    currentOffer = maxHeap.dequeue()
    +    currentOffer
    +  }
    +
    +  override def offerAccepted(assigned: Boolean): Unit = {
    +    if (currentOffer.coresAvailable >= CPUS_PER_TASK && assigned) {
    +      maxHeap.enqueue(currentOffer)
    +    }
    +  }
    +
    +  override def reset(): Unit = {
    +    super.reset
    +    maxHeap = null
    +    currentOffer = null
    +  }
    +}
    +
    +/**
    + * Assign the task to workers with the least available cores. In other 
words, PackedAssigner tries
    + * to schedule tasks to fewer workers.  As a result, there will be idle 
workers without any tasks
    + * assigned if more than required workers are reserved. If the dynamic 
allocator is enabled,
    + * these idle workers will be released by driver. The released resources 
can then be allocated to
    + * other jobs by underling resource manager. This assigner can potentially 
reduce the resource
    + * reservation for a job.
    + */
    +class PackedAssigner extends TaskAssigner {
    +  private var sorted: Seq[OfferState] = _
    +  private var idx = 0
    +  private var currentOffer: OfferState = _
    +
    +  override def init(): Unit = {
    +    idx = 0
    +    sorted = offer.filter(_.coresAvailable >= 
CPUS_PER_TASK).sortBy(_.coresAvailable)
    +  }
    +
    +  override def hasNext: Boolean = idx < sorted.size
    +
    +  override def getNext(): OfferState = {
    +    currentOffer = sorted(idx)
    +    currentOffer
    +  }
    +
    +  override def offerAccepted(assigned: Boolean): Unit = {
    +    if (currentOffer.coresAvailable < CPUS_PER_TASK || !assigned) {
    --- End diff --
    
    Similar to the reason above, if the offer is rejected, we have to move 
forward


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