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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 org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.rdd.RDD
/**
* Usage: MultiBroadcastTest [slices] [numElem]
*/
object MultiBroadcastTest {
def main(args: Array[String]) {
val sparkConf = new SparkConf().setAppName("Multi-Broadcast Test")
val sc = new SparkContext(sparkConf)
val slices = if (args.length > 0) args(0).toInt else 2
val num = if (args.length > 1) args(1).toInt else 1000000
val arr1 = new Array[Int](num)
for (i <- 0 until arr1.length) {
arr1(i) = i
}
val arr2 = new Array[Int](num)
for (i <- 0 until arr2.length) {
arr2(i) = i
}
val barr1 = sc.broadcast(arr1)
val barr2 = sc.broadcast(arr2)
val observedSizes: RDD[(Int, Int)] = sc.parallelize(1 to 10, slices).map { _ =>
(barr1.value.length, barr2.value.length)
}
// Collect the small RDD so we can print the observed sizes locally.
observedSizes.collect().foreach(i => println(i))
sc.stop()
}
}
// scalastyle:on println
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