Github user zhzhan commented on a diff in the pull request:
https://github.com/apache/spark/pull/15541#discussion_r85985739
--- Diff: core/src/main/scala/org/apache/spark/scheduler/TaskAssigner.scala
---
@@ -0,0 +1,232 @@
+/*
+ * 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.{SparkConf, SparkException}
+import org.apache.spark.internal.{config, Logging}
+import org.apache.spark.util.Utils
+
+/** Tracks the current state of the workers with available cores and
assigned task list. */
+private[scheduler] 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)
+
+ def assignTask(task: TaskDescription, cpu: Int): Unit = {
+ if (coresAvailable < cpu) {
+ throw new SparkException(s"Available cores are less than cpu per
task" +
+ s" ($coresAvailable < $cpu)")
+ }
+ tasks += task
+ coresAvailable -= cpu
+ }
+}
+
+/**
+ * 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
+ * performs task assignment given available workers, it first sorts the
candidate tasksets,
+ * and then for each taskset, it takes multiple 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 invokes the init()
+ * of TaskAssigner to initialize the data structure for the round.
+ *
+ * Third, when performing real task assignment, hasNext/next() is used by
TaskScheduler
+ * to check the worker availability and retrieve current offering from
TaskAssigner.
+ *
+ * Fourth, TaskScheduler calls offerAccepted() 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
function tasks to
+ * retrieve all the task assignment information.
+ */
+
+private[scheduler] sealed abstract class TaskAssigner {
+ protected var offer: Seq[OfferState] = _
+ protected var cpuPerTask = 1
+
+ protected def withCpuPerTask(cpuPerTask: Int): TaskAssigner = {
+ this.cpuPerTask = cpuPerTask
+ this
+ }
+
+ /** The currently assigned offers. */
+ final def tasks: Seq[ArrayBuffer[TaskDescription]] = offer.map(_.tasks)
+
+ /**
+ * Invoked at the beginning of resource offering to construct the offer
with the workoffers.
+ * By default, offers is randomly shuffled to avoid always placing tasks
on the same set of
+ * workers.
+ */
+ def construct(workOffer: Seq[WorkerOffer]): Unit = {
+ offer = Random.shuffle(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.
+ *
+ * @param isAccepted whether TaskScheduler assigns a task to current
offer.
+ */
+ def offerAccepted(isAccepted: 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")
+
Utils.classForName(className).getConstructor().newInstance().asInstanceOf[TaskAssigner]
+ .withCpuPerTask(cpuPerTask = conf.getInt("spark.task.cpus", 1))
+ }
+}
+
+/**
+ * Assigns the task to workers with available cores in a roundrobin manner.
+ */
+class RoundRobinAssigner extends TaskAssigner {
+ private var currentOfferIndex = 0
+
+ override def init(): Unit = {
+ currentOfferIndex = 0
+ }
+
+ override def hasNext: Boolean = currentOfferIndex < offer.size
+
+ override def next(): OfferState = {
+ offer(currentOfferIndex)
+ }
+
+ override def offerAccepted(isAccepted: Boolean): Unit = {
+ currentOfferIndex += 1
+ }
+}
+
+/**
+ * Assigns the task to workers with the most available cores. In 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 {
--- End diff --
These two assigner may behave similar in practice. The difference is that
the balanced assigner tries to distribute the work load more aggressively.
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