Github user zhzhan commented on a diff in the pull request:
    --- 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
    + *
    + *
    + *
    + * 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 
    + * 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 = {
    +  }
    +  // The final assigned offer returned to TaskScheduler.
    +  final def tasks: Seq[ArrayBuffer[TaskDescription]] =
    +  // Invoked at the beginning of resource offering to construct the offer 
with the workoffers.
    +  def construct(workOffer: Seq[WorkerOffer]): Unit = {
    +    offer = => 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 
    +  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 = {
    --- End diff --
    My concern is that if we do not use reset, the assigner has to keep 
internal resources until next time, but it is not big overhead.

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