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

    https://github.com/apache/spark/pull/15541#discussion_r84222924
  
    --- Diff: core/src/main/scala/org/apache/spark/scheduler/TaskAssigner.scala 
---
    @@ -0,0 +1,226 @@
    +/*
    + * 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
    +
    +/** Tracks 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 WorkerOffer. */
    +  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, 
when TaskScheduler
    + * perform task assignment given available workers, it first sorts the 
candidate tasksets,
    + * and then for each taskset, it takes a number of rounds to request 
TaskAssigner for task
    + * assignment with different 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.
    + *
    + * Second, before each round of task assignment for a taskset, 
TaskScheduler invoke the init()
    + * of TaskAssigner to initialize the data structure for the round.
    + *
    + * Third, when performing real task assignment, hasNext()/getNext() is 
used by TaskScheduler
    + * to check the worker availability and retrieve current offering from 
TaskAssigner.
    + *
    + * Fourth, 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.
    + *
    + * Fifth, After task assignment is done, TaskScheduler invokes the tasks() 
to
    + * retrieve all the task assignment information.
    + */
    +
    +private[scheduler] abstract class TaskAssigner {
    +  protected var offer: Seq[OfferState] = _
    +  protected var cpuPerTask = 1
    +
    +  protected def withCpuPerTask(cpuPerTask: Int): Unit = {
    +    this.cpuPerTask = cpuPerTask
    +  }
    +
    +  /** 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
    +
    +  /**
    +   * Tests Whether there is offer available to be used inside of one round 
of Taskset assignment.
    +   *  @return  `true` if a subsequent call to `next` will yield an element,
    +   *           `false` otherwise.
    +   */
    +  def hasNext: Boolean
    +
    +  /**
    +   * Produces next worker offer based on the task assignment strategy.
    +   * @return  the next available offer, if `hasNext` is `true`,
    +   *          undefined behavior otherwise.
    +   */
    +  def next(): 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
    +}
    +
    +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")
    +    val className = {
    +      val name = assignerMap.get(assignerName.toLowerCase())
    +      name.getOrElse {
    +        logWarning(s"$assignerName cannot be constructed, fallback to 
default $roundrobin.")
    +        roundrobin
    +      }
    +    }
    +    // The className is valid. No need to catch exceptions.
    +    logInfo(s"Constructing TaskAssigner as $className")
    +    val assigner = Utils.classForName(className)
    +      .getConstructor().newInstance().asInstanceOf[TaskAssigner]
    +    assigner.withCpuPerTask(cpuPerTask = conf.getInt("spark.task.cpus", 1))
    +    assigner
    +  }
    +}
    +
    +/**
    + * Assign the task to workers with available cores in roundrobin manner.
    + */
    +class RoundRobinAssigner extends TaskAssigner {
    +  private var currentOfferIndex = 0
    +
    +  override def construct(workOffer: Seq[WorkerOffer]): Unit = {
    +    offer = Random.shuffle(workOffer.map(o => new OfferState(o)))
    +  }
    +
    +  override def init(): Unit = {
    +    currentOfferIndex = 0
    +  }
    +
    +  override def hasNext: Boolean = currentOfferIndex < offer.size
    +
    +  override def next(): OfferState = {
    +    offer(currentOfferIndex)
    +  }
    +
    +  override def offerAccepted(assigned: Boolean): Unit = {
    +    currentOfferIndex += 1
    +  }
    +}
    +
    +/**
    + * Assign the task to workers with the most available cores. It other 
words, BalancedAssigner tries
    + * to distribute the task across workers in a balanced way. Potentially, 
it may alleviate the
    + * workers' memory pressure as less tasks running on the same workers, 
which also indicates that
    + * the task itself can make use of more computation resources, e.g., 
hyper-thread, across clusters.
    + */
    +class BalancedAssigner extends TaskAssigner {
    +  implicit val ord: Ordering[OfferState] = new Ordering[OfferState] {
    +    def compare(x: OfferState, y: OfferState): Int = {
    +      return Ordering[Int].compare(x.coresAvailable, y.coresAvailable)
    +    }
    +  }
    +  private val maxHeap: PriorityQueue[OfferState] = new 
PriorityQueue[OfferState]()
    +  private var currentOffer: OfferState = _
    +
    +  override def construct(workOffer: Seq[WorkerOffer]): Unit = {
    +    offer = Random.shuffle(workOffer.map(o => new OfferState(o)))
    +  }
    +
    +  override def init(): Unit = {
    +    maxHeap.clear()
    +    offer.filter(_.coresAvailable >= 
cpuPerTask).foreach(maxHeap.enqueue(_))
    +  }
    +
    +  override def hasNext: Boolean = maxHeap.nonEmpty
    +
    +  override def next(): OfferState = {
    +    currentOffer = maxHeap.dequeue()
    +    currentOffer
    +  }
    +
    +  override def offerAccepted(assigned: Boolean): Unit = {
    +    if (currentOffer.coresAvailable >= cpuPerTask && assigned) {
    +      maxHeap.enqueue(currentOffer)
    +    }
    +  }
    +}
    +
    +/**
    + * 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 jobs, which over allocate resources than they need.
    + */
    +class PackedAssigner extends TaskAssigner {
    +  private var sorted: Seq[OfferState] = _
    +  private var currentOfferIndex = 0
    +  private var currentOffer: OfferState = _
    +
    +  override def init(): Unit = {
    +    currentOfferIndex = 0
    +    sorted = offer.filter(_.coresAvailable >= 
cpuPerTask).sortBy(_.coresAvailable)
    --- End diff --
    
    Sounds like the shuffling reason of `BalancedAssigner` can be applied here 
too? If no shuffling, this ensures that initial sorted offers will be 
randomized when cores are the same, right?


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