Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/15541#discussion_r83998540
--- 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)))
+ }
--- End diff --
As you will put offers into the `PriorityQueue`, is it still necessary to
do shuffling?
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