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DrizzleSingleStageExample.scala
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DrizzleSingleStageExample.scala
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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.
*/
// scalastyle:off println
package org.apache.spark.examples
import scala.math.random
import org.apache.spark._
object DrizzleSingleStageExample {
def main(args: Array[String]) {
val numElemsPerPart = 1000L
val numIters = if (args.length > 0) args(0).toInt else 10
val batchSize = if (args.length > 1) args(1).toInt else 5 // if batchSize is 1 it runs baseline
val partitions = if (args.length > 2) args(2).toInt else 4 // number of partitions of the job
val numTrials = if (args.length > 3) args(3).toInt else 5
val numElems = if (args.length > 4) args(4).toLong else (numElemsPerPart * partitions)
println(s"Running DrizzleSingleStageExample for $numIters iterations " +
s"with $batchSize batchSize, $partitions partitions " +
s"for $numTrials trials, $numElems total number of elements")
val conf = new SparkConf().setAppName("DrizzleSingleStageExample")
val sc = new SparkContext(conf)
sc.setLogLevel("WARN")
// Let all the executors join
Thread.sleep(10000)
// Warm up the JVM and copy the JAR out to all the machines etc.
sc.parallelize(0 until sc.getExecutorMemoryStatus.size,
sc.getExecutorMemoryStatus.size).foreach { x =>
Thread.sleep(1)
}
val rdd = sc.parallelize(1L to numElems, partitions).map(i => 2L * i).cache()
rdd.count()
val sumFunc = (iter: Iterator[Long]) => iter.reduceLeft(_ + _)
for (j <- 0 until numTrials) {
val begin = System.nanoTime
val sums = if (batchSize == 1) {
(0 until numIters).map { i =>
sc.runJob(rdd.map(x => x + i), sumFunc)
}
} else {
val numBatches = math.ceil(numIters.toDouble / batchSize).toInt
val funcs = Seq.fill(batchSize)(sumFunc)
// Use a different RDD for each trial
val rdds = (0 until batchSize).map { i => rdd.map(x => x + i) }
(0 until numBatches).flatMap { b =>
val outs = sc.runJobs(rdds, funcs)
outs
}
}
val end = System.nanoTime
println("Got sum " + sums.map(x => x.sum).sum)
println("Drizzle: Running " + numIters + " iters " + batchSize + " batchSize took " +
(end-begin)/1e6 + " ms")
}
sc.stop()
}
}