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/*
* 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.local
import org.scalatest.FunSuite
import org.scalatest.BeforeAndAfter
import org.apache.spark._
import org.apache.spark.scheduler._
import org.apache.spark.scheduler.cluster._
import scala.collection.mutable.ArrayBuffer
import scala.collection.mutable.{ConcurrentMap, HashMap}
import java.util.concurrent.Semaphore
import java.util.concurrent.CountDownLatch
import java.util.Properties
class Lock() {
var finished = false
def jobWait() = {
synchronized {
while(!finished) {
this.wait()
}
}
}
def jobFinished() = {
synchronized {
finished = true
this.notifyAll()
}
}
}
object TaskThreadInfo {
val threadToLock = HashMap[Int, Lock]()
val threadToRunning = HashMap[Int, Boolean]()
val threadToStarted = HashMap[Int, CountDownLatch]()
}
/*
* 1. each thread contains one job.
* 2. each job contains one stage.
* 3. each stage only contains one task.
* 4. each task(launched) must be lanched orderly(using threadToStarted) to make sure
* it will get cpu core resource, and will wait to finished after user manually
* release "Lock" and then cluster will contain another free cpu cores.
* 5. each task(pending) must use "sleep" to make sure it has been added to taskSetManager queue,
* thus it will be scheduled later when cluster has free cpu cores.
*/
class LocalSchedulerSuite extends FunSuite with LocalSparkContext {
def createThread(threadIndex: Int, poolName: String, sc: SparkContext, sem: Semaphore) {
TaskThreadInfo.threadToRunning(threadIndex) = false
val nums = sc.parallelize(threadIndex to threadIndex, 1)
TaskThreadInfo.threadToLock(threadIndex) = new Lock()
TaskThreadInfo.threadToStarted(threadIndex) = new CountDownLatch(1)
new Thread {
if (poolName != null) {
sc.setLocalProperty("spark.scheduler.pool", poolName)
}
override def run() {
val ans = nums.map(number => {
TaskThreadInfo.threadToRunning(number) = true
TaskThreadInfo.threadToStarted(number).countDown()
TaskThreadInfo.threadToLock(number).jobWait()
TaskThreadInfo.threadToRunning(number) = false
number
}).collect()
assert(ans.toList === List(threadIndex))
sem.release()
}
}.start()
}
test("Local FIFO scheduler end-to-end test") {
System.setProperty("spark.scheduler.mode", "FIFO")
sc = new SparkContext("local[4]", "test")
val sem = new Semaphore(0)
createThread(1,null,sc,sem)
TaskThreadInfo.threadToStarted(1).await()
createThread(2,null,sc,sem)
TaskThreadInfo.threadToStarted(2).await()
createThread(3,null,sc,sem)
TaskThreadInfo.threadToStarted(3).await()
createThread(4,null,sc,sem)
TaskThreadInfo.threadToStarted(4).await()
// thread 5 and 6 (stage pending)must meet following two points
// 1. stages (taskSetManager) of jobs in thread 5 and 6 should be add to taskSetManager
// queue before executing TaskThreadInfo.threadToLock(1).jobFinished()
// 2. priority of stage in thread 5 should be prior to priority of stage in thread 6
// So I just use "sleep" 1s here for each thread.
// TODO: any better solution?
createThread(5,null,sc,sem)
Thread.sleep(1000)
createThread(6,null,sc,sem)
Thread.sleep(1000)
assert(TaskThreadInfo.threadToRunning(1) === true)
assert(TaskThreadInfo.threadToRunning(2) === true)
assert(TaskThreadInfo.threadToRunning(3) === true)
assert(TaskThreadInfo.threadToRunning(4) === true)
assert(TaskThreadInfo.threadToRunning(5) === false)
assert(TaskThreadInfo.threadToRunning(6) === false)
TaskThreadInfo.threadToLock(1).jobFinished()
TaskThreadInfo.threadToStarted(5).await()
assert(TaskThreadInfo.threadToRunning(1) === false)
assert(TaskThreadInfo.threadToRunning(2) === true)
assert(TaskThreadInfo.threadToRunning(3) === true)
assert(TaskThreadInfo.threadToRunning(4) === true)
assert(TaskThreadInfo.threadToRunning(5) === true)
assert(TaskThreadInfo.threadToRunning(6) === false)
TaskThreadInfo.threadToLock(3).jobFinished()
TaskThreadInfo.threadToStarted(6).await()
assert(TaskThreadInfo.threadToRunning(1) === false)
assert(TaskThreadInfo.threadToRunning(2) === true)
assert(TaskThreadInfo.threadToRunning(3) === false)
assert(TaskThreadInfo.threadToRunning(4) === true)
assert(TaskThreadInfo.threadToRunning(5) === true)
assert(TaskThreadInfo.threadToRunning(6) === true)
TaskThreadInfo.threadToLock(2).jobFinished()
TaskThreadInfo.threadToLock(4).jobFinished()
TaskThreadInfo.threadToLock(5).jobFinished()
TaskThreadInfo.threadToLock(6).jobFinished()
sem.acquire(6)
}
test("Local fair scheduler end-to-end test") {
sc = new SparkContext("local[8]", "LocalSchedulerSuite")
val sem = new Semaphore(0)
System.setProperty("spark.scheduler.mode", "FAIR")
val xmlPath = getClass.getClassLoader.getResource("fairscheduler.xml").getFile()
System.setProperty("spark.scheduler.allocation.file", xmlPath)
createThread(10,"1",sc,sem)
TaskThreadInfo.threadToStarted(10).await()
createThread(20,"2",sc,sem)
TaskThreadInfo.threadToStarted(20).await()
createThread(30,"3",sc,sem)
TaskThreadInfo.threadToStarted(30).await()
assert(TaskThreadInfo.threadToRunning(10) === true)
assert(TaskThreadInfo.threadToRunning(20) === true)
assert(TaskThreadInfo.threadToRunning(30) === true)
createThread(11,"1",sc,sem)
TaskThreadInfo.threadToStarted(11).await()
createThread(21,"2",sc,sem)
TaskThreadInfo.threadToStarted(21).await()
createThread(31,"3",sc,sem)
TaskThreadInfo.threadToStarted(31).await()
assert(TaskThreadInfo.threadToRunning(11) === true)
assert(TaskThreadInfo.threadToRunning(21) === true)
assert(TaskThreadInfo.threadToRunning(31) === true)
createThread(12,"1",sc,sem)
TaskThreadInfo.threadToStarted(12).await()
createThread(22,"2",sc,sem)
TaskThreadInfo.threadToStarted(22).await()
createThread(32,"3",sc,sem)
assert(TaskThreadInfo.threadToRunning(12) === true)
assert(TaskThreadInfo.threadToRunning(22) === true)
assert(TaskThreadInfo.threadToRunning(32) === false)
TaskThreadInfo.threadToLock(10).jobFinished()
TaskThreadInfo.threadToStarted(32).await()
assert(TaskThreadInfo.threadToRunning(32) === true)
//1. Similar with above scenario, sleep 1s for stage of 23 and 33 to be added to taskSetManager
// queue so that cluster will assign free cpu core to stage 23 after stage 11 finished.
//2. priority of 23 and 33 will be meaningless as using fair scheduler here.
createThread(23,"2",sc,sem)
createThread(33,"3",sc,sem)
Thread.sleep(1000)
TaskThreadInfo.threadToLock(11).jobFinished()
TaskThreadInfo.threadToStarted(23).await()
assert(TaskThreadInfo.threadToRunning(23) === true)
assert(TaskThreadInfo.threadToRunning(33) === false)
TaskThreadInfo.threadToLock(12).jobFinished()
TaskThreadInfo.threadToStarted(33).await()
assert(TaskThreadInfo.threadToRunning(33) === true)
TaskThreadInfo.threadToLock(20).jobFinished()
TaskThreadInfo.threadToLock(21).jobFinished()
TaskThreadInfo.threadToLock(22).jobFinished()
TaskThreadInfo.threadToLock(23).jobFinished()
TaskThreadInfo.threadToLock(30).jobFinished()
TaskThreadInfo.threadToLock(31).jobFinished()
TaskThreadInfo.threadToLock(32).jobFinished()
TaskThreadInfo.threadToLock(33).jobFinished()
sem.acquire(11)
}
}
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