Spark 与 Hadoop 关于 TeraGen/TeraSort 的对比实验(包含源代码)

自从 Hadoop 问世以来，MapReduce 在很长时间内都是排序基准测试的纪录保持者，但这一垄断在最近被基于内存计算的 Spark 打破了。在今年Databricks与AWS一起完成的一个Daytona Gray类别的Sort Benchmark中，Spark 完胜 Hadoop MapReduce：“1/10计算资源，1/3耗时”。这是个很有意思的对比实验，因此笔者也在一个小规模集群上做了一个微缩版的类似试验。

1、Hadoop 与 Spark 集群环境完全相同：

- Hadoop 2.2.0

- Spark 1.0

- 5 节点集群:

    node1: NameNode, ResourceManger 

    node2 - node 5: NodeManager

- Hardware:

8 core cpu, 32 GB memory, 400 GB disk

2、排序数据规模：100 GB

3、Hadoop 排序：

3.1 TeraGen:

在4个slaves上共启动了120个mapper：

hadoop jar ${HADOOP_EXAMPLE_JAR_PATH} teragen -Dmapred.map.tasks=120 ${TERAGEN_ROW} ${TERAGEN_OUTPUT}

3.2 TeraSort:

在4个slaves上共启动了32个reducer：

hadoop jar ${HADOOP_EXAMPLE_JAR_PATH} terasort -Dmapred.reduce.tasks=32 ${TERAGEN_OUTPUT} ${TERASORT_OUTPUT}

3.3 生成100 GB测试数据、完成排序总共花费的时间：

总计：6723 秒

4、Spark 排序：

4.1 源代码：

4.1.1 来源：

https://github.com/apache/spark/pull/1242
https://github.com/rxin/spark/tree/adcae69145905162fa3b6932f70be2c932f95f87/examples/src/main/scala/org/apache/spark/examples/terasort

4.1.2 为了便于大家阅读源代码，我把源代码也附于本文文末(已做些许更改)

4.2 生成测试数据、完成排序(如果输出文件格式为text file，则排序结果的文件总大小为309.2 GB)总共花费的时间：

- 试验一：

  * 任务提交参数：num-executors: 4， executor-memory: 8g， executor-cores: 4

  * 输出文件格式：Sequence File

  * 输出文件所占空间为：20.8 GB

  * 总时间为:  2203 秒

- 试验二：

  * 任务提交参数：num-executors: 4， executor-memory: 16g， executor-cores: 6

  * 输出文件格式：Text File

  * 输出文件所占空间为：309.2 GB

  * 总时间为: 9849 秒

- 试验三：

  * 任务提交参数：num-executors: 4， executor-memory: 16g， executor-cores: 6

  * 输出文件格式：Sequence File

  * 输出文件所占空间为：20.8 GB

  * 总时间为: 2212 秒

- 试验四：

  * 任务提交参数：num-executors: 8， executor-memory: 7g， executor-cores: 3

  * 输出文件格式：Sequence File

  * 输出文件所占空间为：20.8 GB

  * 总时间为: 1213 秒

- 试验五：

  * 任务提交参数：num-executors: 28， executor-memory: 2g， executor-cores: 1

  * 输出文件格式：Sequence File

  * 输出文件所占空间为：20.8 GB

  * 总时间为: 483 秒

- 试验六：

  * 任务提交参数：num-executors: 56， executor-memory: 1g， executor-cores: 1

  * 输出文件格式：Sequence File

  * 输出文件所占空间为：20.8 GB

  * 总时间为: 434 秒

5、小结：

5.1 Hadoop 与 Spark 比较：

当然，执行过程肯定还有调优空间，但 Spark 明显快于 Hadoop MapReduce。这个结果也很正常：这是内存对于硬盘的胜利。

5.2 Spark 几次试验之间的比较：

- 输出结果为Sequence file时，要大大快于输出结果为 Text file时。因为Sequence file大大压缩了输出文件大小，也减少了大量 disk IO，这样也就很大地缩短了执行时间

- 如果单个executor的计算并不需要过大的内存，不如降低单个executor的内存共给量，同时增加executor的并发数（如果任务适合并发）

- 一旦单个worker的内存与cpu已经被充分利用，而且并发的executor数也比较合理，那么再进一步分割executor数并不会增加效率

附：Spark Sort 源代码

a. GenSort.scala

package scala.spark.examples.terasort/** * 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. */import org.apache.hadoop.io.{ BytesWritable, NullWritable }import org.apache.hadoop.io.compress.BZip2Codecimport org.apache.spark.SparkContextimport org.apache.spark.SparkContext._import org.apache.spark._import SparkContext._object GenSort {  def main(args: Array[String]) {    if (args.length < 3) {      println("usage:")      println("MASTER=[spark-master] bin/run-example org.apache.spark.examples.terasort.GenSort " +        " [num-parts] [records-per-part] [output-path]")      System.exit(0)    }    val master = sys.env.getOrElse("MASTER", "local")    val parts = args(0).toInt    val recordsPerPartition = args(1).toInt    val numRecords = parts.toLong * recordsPerPartition.toLong    val output = args(2)    println(s"Generating $numRecords records on $parts partitions")    println(s"Output path: $output")//    val sc = new SparkContext(master, "GenSort")    val conf = new SparkConf().setAppName("GenSort")    val sc = new SparkContext(conf)    val dataset = sc.parallelize(1 to parts, parts).mapPartitionsWithIndex {      case (index, _) =>        val one = new Unsigned16(1)        val firstRecordNumber = new Unsigned16(index * recordsPerPartition)        val recordsToGenerate = new Unsigned16(recordsPerPartition)        val recordNumber = new Unsigned16(firstRecordNumber)        val lastRecordNumber = new Unsigned16(firstRecordNumber)        lastRecordNumber.add(recordsToGenerate)        val rand = Random16.skipAhead(firstRecordNumber)        val row: Array[Byte] = new Array[Byte](100)        Iterator.tabulate(recordsPerPartition) { offset =>          Random16.nextRand(rand)          generateRecord(row, rand, recordNumber)          recordNumber.add(one)          row        }    }    // Save output result as text file    dataset.map(row => (NullWritable.get(), new BytesWritable(row))).saveAsTextFile(output)        // Save output result as sequence file//    dataset.map(row => (NullWritable.get(), new BytesWritable(row)))//      .saveAsSequenceFile(output, Some(classOf[BZip2Codec]))  }  /**   * Generate a binary record suitable for all sort benchmarks except PennySort.   *   * @param recBuf record to return   */  def generateRecord(recBuf: Array[Byte], rand: Unsigned16, recordNumber: Unsigned16): Unit = {    // Generate the 10-byte key using the high 10 bytes of the 128-bit random number    var i = 0    while (i < 10) {      recBuf(i) = rand.getByte(i)      i += 1    }    // Add 2 bytes of "break"    recBuf(10) = 0x00.toByte    recBuf(11) = 0x11.toByte    // Convert the 128-bit record number to 32 bits of ascii hexadecimal    // as the next 32 bytes of the record.    i = 0    while (i < 32) {      recBuf(12 + i) = recordNumber.getHexDigit(i).toByte      i += 1    }    // Add 4 bytes of "break" data    recBuf(44) = 0x88.toByte    recBuf(45) = 0x99.toByte    recBuf(46) = 0xAA.toByte    recBuf(47) = 0xBB.toByte    // Add 48 bytes of filler based on low 48 bits of random number    i = 0    while (i < 12) {      val v = rand.getHexDigit(20 + i).toByte      recBuf(48 + i * 4) = v      recBuf(49 + i * 4) = v      recBuf(50 + i * 4) = v      recBuf(51 + i * 4) = v      i += 1    }    // Add 4 bytes of "break" data    recBuf(96) = 0xCC.toByte    recBuf(97) = 0xDD.toByte    recBuf(98) = 0xEE.toByte    recBuf(99) = 0xFF.toByte  }}

b. Random16.java

/** * 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 scala.spark.examples.terasort;/** * This file is copied from Hadoop package org.apache.hadoop.examples.terasort. *//** * This class implements a 128-bit linear congruential generator. Specifically, * if X0 is the most recently issued 128-bit random number (or a seed of 0 if no * random number has already been generated, the next number to be generated, * X1, is equal to: X1 = (a * X0 + c) mod 2**128 where a is * 47026247687942121848144207491837523525 or 0x2360ed051fc65da44385df649fccf645 * and c is 98910279301475397889117759788405497857 or * 0x4a696d47726179524950202020202001 The coefficient "a" is suggested by: * Pierre L'Ecuyer, "Tables of linear congruential generators of different sizes * and good lattice structure", Mathematics of Computation, 68 pp. 249 - 260 * (1999) * http://www.ams.org/mcom/1999-68-225/S0025-5718-99-00996-5/S0025-5718-99 * -00996-5.pdf The constant "c" meets the simple suggestion by the same * reference that it be odd. *  * There is also a facility for quickly advancing the state of the generator by * a fixed number of steps - this facilitates parallel generation. *  * This is based on 1.0 of rand16.c from Chris Nyberg * <[e-mail]>. */class Random16 {/** * The "Gen" array contain powers of 2 of the linear congruential generator. * The index 0 struct contain the "a" coefficient and "c" constant for the * generator. That is, the generator is: f(x) = (Gen[0].a * x + Gen[0].c) * mod 2**128 *  * All structs after the first contain an "a" and "c" that comprise the * square of the previous function. *  * f**2(x) = (Gen[1].a * x + Gen[1].c) mod 2**128 f**4(x) = (Gen[2].a * x + * Gen[2].c) mod 2**128 f**8(x) = (Gen[3].a * x + Gen[3].c) mod 2**128 ... */private static class RandomConstant {final Unsigned16 a;final Unsigned16 c;public RandomConstant(String left, String right) {a = new Unsigned16(left);c = new Unsigned16(right);}}private static final RandomConstant[] genArray = new RandomConstant[] {/* [ 0] */new RandomConstant("2360ed051fc65da44385df649fccf645","4a696d47726179524950202020202001"),/* [ 1] */new RandomConstant("17bce35bdf69743c529ed9eb20e0ae99","95e0e48262b3edfe04479485c755b646"),/* [ 2] */new RandomConstant("f4dd417327db7a9bd194dfbe42d45771","882a02c315362b60765f100068b33a1c"),/* [ 3] */new RandomConstant("6347af777a7898f6d1a2d6f33505ffe1","5efc4abfaca23e8ca8edb1f2dfbf6478"),/* [ 4] */new RandomConstant("b6a4239f3b315f84f6ef6d3d288c03c1","f25bd15439d16af594c1b1bafa6239f0"),/* [ 5] */new RandomConstant("2c82901ad1cb0cd182b631ba6b261781","89ca67c29c9397d59c612596145db7e0"),/* [ 6] */new RandomConstant("dab03f988288676ee49e66c4d2746f01","8b6ae036713bd578a8093c8eae5c7fc0"),/* [ 7] */new RandomConstant("602167331d86cf5684fe009a6d09de01","98a2542fd23d0dbdff3b886cdb1d3f80"),/* [ 8] */new RandomConstant("61ecb5c24d95b058f04c80a23697bc01","954db923fdb7933e947cd1edcecb7f00"),/* [ 9] */new RandomConstant("4a5c31e0654c28aa60474e83bf3f7801","00be4a36657c98cd204e8c8af7dafe00"),/* [ 10] */new RandomConstant("ae4f079d54fbece1478331d3c6bef001","991965329dccb28d581199ab18c5fc00"),/* [ 11] */new RandomConstant("101b8cb830c7cb927ff1ed50ae7de001","e1a8705b63ad5b8cd6c3d268d5cbf800"),/* [ 12] */new RandomConstant("f54a27fc056b00e7563f3505e0fbc001","2b657bbfd6ed9d632079e70c3c97f000"),/* [ 13] */new RandomConstant("df8a6fc1a833d201f98d719dd1f78001","59b60ee4c52fa49e9fe90682bd2fe000"),/* [ 14] */new RandomConstant("5480a5015f101a4ea7e3f183e3ef0001","cc099c88030679464fe86aae8a5fc000"),/* [ 15] */new RandomConstant("a498509e76e5d7925f539c28c7de0001","06b9abff9f9f33dd30362c0154bf8000"),/* [ 16] */new RandomConstant("0798a3d8b10dc72e60121cd58fbc0001","e296707121688d5a0260b293a97f0000"),/* [ 17] */new RandomConstant("1647d1e78ec02e665fafcbbb1f780001","189ffc4701ff23cb8f8acf6b52fe0000"),/* [ 18] */new RandomConstant("a7c982285e72bf8c0c8ddfb63ef00001","5141110ab208fb9d61fb47e6a5fc0000"),/* [ 19] */new RandomConstant("3eb78ee8fb8c56dbc5d4e06c7de00001","3c97caa62540f2948d8d340d4bf80000"),/* [ 20] */new RandomConstant("72d03b6f4681f2f9fe8e44d8fbc00001","1b25cb9cfe5a0c963174f91a97f00000"),/* [ 21] */new RandomConstant("ea85f81e4f502c9bc8ae99b1f7800001","0c644570b4a487103c5436352fe00000"),/* [ 22] */new RandomConstant("629c320db08b00c6bfa57363ef000001","3d0589c28869472bde517c6a5fc00000"),/* [ 23] */new RandomConstant("c5c4b9ce268d074a386be6c7de000001","bc95e5ab36477e65534738d4bf800000"),/* [ 24] */new RandomConstant("f30bbbbed1596187555bcd8fbc000001","ddb02ff72a031c01011f71a97f000000"),/* [ 25] */new RandomConstant("4a1000fb26c9eeda3cc79b1f78000001","2561426086d9acdb6c82e352fe000000"),/* [ 26] */new RandomConstant("89fb5307f6bf8ce2c1cf363ef0000001","64a788e3c118ed1c8215c6a5fc000000"),/* [ 27] */new RandomConstant("830b7b3358a5d67ea49e6c7de0000001","e65ea321908627cfa86b8d4bf8000000"),/* [ 28] */new RandomConstant("fd8a51da91a69fe1cd3cd8fbc0000001","53d27225604d85f9e1d71a97f0000000"),/* [ 29] */new RandomConstant("901a48b642b90b55aa79b1f780000001","ca5ec7a3ed1fe55e07ae352fe0000000"),/* [ 30] */new RandomConstant("118cdefdf32144f394f363ef00000001","4daebb2e085330651f5c6a5fc0000000"),/* [ 31] */new RandomConstant("0a88c0a91cff430829e6c7de00000001","9d6f1a00a8f3f76e7eb8d4bf80000000"),/* [ 32] */new RandomConstant("433bef4314f16a9453cd8fbc00000001","158c62f2b31e496dfd71a97f00000000"),/* [ 33] */new RandomConstant("c294b02995ae6738a79b1f7800000001","290e84a2eb15fd1ffae352fe00000000"),/* [ 34] */new RandomConstant("913575e0da8b16b14f363ef000000001","e3dc1bfbe991a34ff5c6a5fc00000000"),/* [ 35] */new RandomConstant("2f61b9f871cf4e629e6c7de000000001","ddf540d020b9eadfeb8d4bf800000000"),/* [ 36] */new RandomConstant("78d26ccbd68320c53cd8fbc000000001","8ee4950177ce66bfd71a97f000000000"),/* [ 37] */new RandomConstant("8b7ebd037898518a79b1f78000000001","39e0f787c907117fae352fe000000000"),/* [ 38] */new RandomConstant("0b5507b61f78e314f363ef0000000001","659d2522f7b732ff5c6a5fc000000000"),/* [ 39] */new RandomConstant("4f884628f812c629e6c7de0000000001","9e8722938612a5feb8d4bf8000000000"),/* [ 40] */new RandomConstant("be896744d4a98c53cd8fbc0000000001","e941a65d66b64bfd71a97f0000000000"),/* [ 41] */new RandomConstant("daf63a553b6318a79b1f780000000001","7b50d19437b097fae352fe0000000000"),/* [ 42] */new RandomConstant("2d7a23d8bf06314f363ef00000000001","59d7b68e18712ff5c6a5fc0000000000"),/* [ 43] */new RandomConstant("392b046a9f0c629e6c7de00000000001","4087bab2d5225feb8d4bf80000000000"),/* [ 44] */new RandomConstant("eb30fbb9c218c53cd8fbc00000000001","b470abc03b44bfd71a97f00000000000"),/* [ 45] */new RandomConstant("b9cdc30594318a79b1f7800000000001","366630eaba897fae352fe00000000000"),/* [ 46] */new RandomConstant("014ab453686314f363ef000000000001","a2dfc77e8512ff5c6a5fc00000000000"),/* [ 47] */new RandomConstant("395221c7d0c629e6c7de000000000001","1e0d25a14a25feb8d4bf800000000000"),/* [ 48] */new RandomConstant("4d972813a18c53cd8fbc000000000001","9d50a5d3944bfd71a97f000000000000"),/* [ 49] */new RandomConstant("06f9e2374318a79b1f78000000000001","bf7ab5eb2897fae352fe000000000000"),/* [ 50] */new RandomConstant("bd220cae86314f363ef0000000000001","925b14e6512ff5c6a5fc000000000000"),/* [ 51] */new RandomConstant("36fd3a5d0c629e6c7de0000000000001","724cce0ca25feb8d4bf8000000000000"),/* [ 52] */new RandomConstant("60def8ba18c53cd8fbc0000000000001","1af42d1944bfd71a97f0000000000000"),/* [ 53] */new RandomConstant("8d500174318a79b1f780000000000001","0f529e32897fae352fe0000000000000"),/* [ 54] */new RandomConstant("48e842e86314f363ef00000000000001","844e4c6512ff5c6a5fc0000000000000"),/* [ 55] */new RandomConstant("4af185d0c629e6c7de00000000000001","9f40d8ca25feb8d4bf80000000000000"),/* [ 56] */new RandomConstant("7a670ba18c53cd8fbc00000000000001","9912b1944bfd71a97f00000000000000"),/* [ 57] */new RandomConstant("86de174318a79b1f7800000000000001","9c69632897fae352fe00000000000000"),/* [ 58] */new RandomConstant("55fc2e86314f363ef000000000000001","e1e2c6512ff5c6a5fc00000000000000"),/* [ 59] */new RandomConstant("ccf85d0c629e6c7de000000000000001","68058ca25feb8d4bf800000000000000"),/* [ 60] */new RandomConstant("1df0ba18c53cd8fbc000000000000001","610b1944bfd71a97f000000000000000"),/* [ 61] */new RandomConstant("4be174318a79b1f78000000000000001","061632897fae352fe000000000000000"),/* [ 62] */new RandomConstant("d7c2e86314f363ef0000000000000001","1c2c6512ff5c6a5fc000000000000000"),/* [ 63] */new RandomConstant("af85d0c629e6c7de0000000000000001","7858ca25feb8d4bf8000000000000000"),/* [ 64] */new RandomConstant("5f0ba18c53cd8fbc0000000000000001","f0b1944bfd71a97f0000000000000000"),/* [ 65] */new RandomConstant("be174318a79b1f780000000000000001","e1632897fae352fe0000000000000000"),/* [ 66] */new RandomConstant("7c2e86314f363ef00000000000000001","c2c6512ff5c6a5fc0000000000000000"),/* [ 67] */new RandomConstant("f85d0c629e6c7de00000000000000001","858ca25feb8d4bf80000000000000000"),/* [ 68] */new RandomConstant("f0ba18c53cd8fbc00000000000000001","0b1944bfd71a97f00000000000000000"),/* [ 69] */new RandomConstant("e174318a79b1f7800000000000000001","1632897fae352fe00000000000000000"),/* [ 70] */new RandomConstant("c2e86314f363ef000000000000000001","2c6512ff5c6a5fc00000000000000000"),/* [ 71] */new RandomConstant("85d0c629e6c7de000000000000000001","58ca25feb8d4bf800000000000000000"),/* [ 72] */new RandomConstant("0ba18c53cd8fbc000000000000000001","b1944bfd71a97f000000000000000000"),/* [ 73] */new RandomConstant("174318a79b1f78000000000000000001","632897fae352fe000000000000000000"),/* [ 74] */new RandomConstant("2e86314f363ef0000000000000000001","c6512ff5c6a5fc000000000000000000"),/* [ 75] */new RandomConstant("5d0c629e6c7de0000000000000000001","8ca25feb8d4bf8000000000000000000"),/* [ 76] */new RandomConstant("ba18c53cd8fbc0000000000000000001","1944bfd71a97f0000000000000000000"),/* [ 77] */new RandomConstant("74318a79b1f780000000000000000001","32897fae352fe0000000000000000000"),/* [ 78] */new RandomConstant("e86314f363ef00000000000000000001","6512ff5c6a5fc0000000000000000000"),/* [ 79] */new RandomConstant("d0c629e6c7de00000000000000000001","ca25feb8d4bf80000000000000000000"),/* [ 80] */new RandomConstant("a18c53cd8fbc00000000000000000001","944bfd71a97f00000000000000000000"),/* [ 81] */new RandomConstant("4318a79b1f7800000000000000000001","2897fae352fe00000000000000000000"),/* [ 82] */new RandomConstant("86314f363ef000000000000000000001","512ff5c6a5fc00000000000000000000"),/* [ 83] */new RandomConstant("0c629e6c7de000000000000000000001","a25feb8d4bf800000000000000000000"),/* [ 84] */new RandomConstant("18c53cd8fbc000000000000000000001","44bfd71a97f000000000000000000000"),/* [ 85] */new RandomConstant("318a79b1f78000000000000000000001","897fae352fe000000000000000000000"),/* [ 86] */new RandomConstant("6314f363ef0000000000000000000001","12ff5c6a5fc000000000000000000000"),/* [ 87] */new RandomConstant("c629e6c7de0000000000000000000001","25feb8d4bf8000000000000000000000"),/* [ 88] */new RandomConstant("8c53cd8fbc0000000000000000000001","4bfd71a97f0000000000000000000000"),/* [ 89] */new RandomConstant("18a79b1f780000000000000000000001","97fae352fe0000000000000000000000"),/* [ 90] */new RandomConstant("314f363ef00000000000000000000001","2ff5c6a5fc0000000000000000000000"),/* [ 91] */new RandomConstant("629e6c7de00000000000000000000001","5feb8d4bf80000000000000000000000"),/* [ 92] */new RandomConstant("c53cd8fbc00000000000000000000001","bfd71a97f00000000000000000000000"),/* [ 93] */new RandomConstant("8a79b1f7800000000000000000000001","7fae352fe00000000000000000000000"),/* [ 94] */new RandomConstant("14f363ef000000000000000000000001","ff5c6a5fc00000000000000000000000"),/* [ 95] */new RandomConstant("29e6c7de000000000000000000000001","feb8d4bf800000000000000000000000"),/* [ 96] */new RandomConstant("53cd8fbc000000000000000000000001","fd71a97f000000000000000000000000"),/* [ 97] */new RandomConstant("a79b1f78000000000000000000000001","fae352fe000000000000000000000000"),/* [ 98] */new RandomConstant("4f363ef0000000000000000000000001","f5c6a5fc000000000000000000000000"),/* [ 99] */new RandomConstant("9e6c7de0000000000000000000000001","eb8d4bf8000000000000000000000000"),/* [100] */new RandomConstant("3cd8fbc0000000000000000000000001","d71a97f0000000000000000000000000"),/* [101] */new RandomConstant("79b1f780000000000000000000000001","ae352fe0000000000000000000000000"),/* [102] */new RandomConstant("f363ef00000000000000000000000001","5c6a5fc0000000000000000000000000"),/* [103] */new RandomConstant("e6c7de00000000000000000000000001","b8d4bf80000000000000000000000000"),/* [104] */new RandomConstant("cd8fbc00000000000000000000000001","71a97f00000000000000000000000000"),/* [105] */new RandomConstant("9b1f7800000000000000000000000001","e352fe00000000000000000000000000"),/* [106] */new RandomConstant("363ef000000000000000000000000001","c6a5fc00000000000000000000000000"),/* [107] */new RandomConstant("6c7de000000000000000000000000001","8d4bf800000000000000000000000000"),/* [108] */new RandomConstant("d8fbc000000000000000000000000001","1a97f000000000000000000000000000"),/* [109] */new RandomConstant("b1f78000000000000000000000000001","352fe000000000000000000000000000"),/* [110] */new RandomConstant("63ef0000000000000000000000000001","6a5fc000000000000000000000000000"),/* [111] */new RandomConstant("c7de0000000000000000000000000001","d4bf8000000000000000000000000000"),/* [112] */new RandomConstant("8fbc0000000000000000000000000001","a97f0000000000000000000000000000"),/* [113] */new RandomConstant("1f780000000000000000000000000001","52fe0000000000000000000000000000"),/* [114] */new RandomConstant("3ef00000000000000000000000000001","a5fc0000000000000000000000000000"),/* [115] */new RandomConstant("7de00000000000000000000000000001","4bf80000000000000000000000000000"),/* [116] */new RandomConstant("fbc00000000000000000000000000001","97f00000000000000000000000000000"),/* [117] */new RandomConstant("f7800000000000000000000000000001","2fe00000000000000000000000000000"),/* [118] */new RandomConstant("ef000000000000000000000000000001","5fc00000000000000000000000000000"),/* [119] */new RandomConstant("de000000000000000000000000000001","bf800000000000000000000000000000"),/* [120] */new RandomConstant("bc000000000000000000000000000001","7f000000000000000000000000000000"),/* [121] */new RandomConstant("78000000000000000000000000000001","fe000000000000000000000000000000"),/* [122] */new RandomConstant("f0000000000000000000000000000001","fc000000000000000000000000000000"),/* [123] */new RandomConstant("e0000000000000000000000000000001","f8000000000000000000000000000000"),/* [124] */new RandomConstant("c0000000000000000000000000000001","f0000000000000000000000000000000"),/* [125] */new RandomConstant("80000000000000000000000000000001","e0000000000000000000000000000000"),/* [126] */new RandomConstant("00000000000000000000000000000001","c0000000000000000000000000000000"),/* [127] */new RandomConstant("00000000000000000000000000000001","80000000000000000000000000000000") };/** * generate the random number that is "advance" steps from an initial random * number of 0. This is done by starting with 0, and then advancing the by * the appropriate powers of 2 of the linear congruential generator. */public static Unsigned16 skipAhead(Unsigned16 advance) {Unsigned16 result = new Unsigned16();long bit_map;bit_map = advance.getLow8();for (int i = 0; bit_map != 0 && i < 64; i++) {if ((bit_map & (1L << i)) != 0) {/* * advance random number by f**(2**i) (x) */result.multiply(genArray[i].a);result.add(genArray[i].c);bit_map &= ~(1L << i);}}bit_map = advance.getHigh8();for (int i = 0; bit_map != 0 && i < 64; i++) {if ((bit_map & (1L << i)) != 0) {/* * advance random number by f**(2**(i + 64)) (x) */result.multiply(genArray[i + 64].a);result.add(genArray[i + 64].c);bit_map &= ~(1L << i);}}return result;}/** * Generate the next 16 byte random number. */public static void nextRand(Unsigned16 rand) {/* * advance the random number forward once using the linear congruential * generator, and then return the new random number */rand.multiply(genArray[0].a);rand.add(genArray[0].c);}}

c. Unsigned16.java

/** * 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 scala.spark.examples.terasort;import java.io.DataInput;import java.io.DataOutput;import java.io.IOException;import org.apache.hadoop.io.Writable;/** * This file is copied from Hadoop package org.apache.hadoop.examples.terasort. *//** * An unsigned 16 byte integer class that supports addition, multiplication, and * left shifts. */class Unsigned16 implements Writable {private long hi8;private long lo8;public Unsigned16() {hi8 = 0;lo8 = 0;}public Unsigned16(long l) {hi8 = 0;lo8 = l;}public Unsigned16(Unsigned16 other) {hi8 = other.hi8;lo8 = other.lo8;}@Overridepublic boolean equals(Object o) {if (o instanceof Unsigned16) {Unsigned16 other = (Unsigned16) o;return other.hi8 == hi8 && other.lo8 == lo8;}return false;}@Overridepublic int hashCode() {return (int) lo8;}/** * Parse a hex string *  * @param s *            the hex string */public Unsigned16(String s) throws NumberFormatException {set(s);}/** * Set the number from a hex string *  * @param s *            the number in hexadecimal * @throws NumberFormatException *             if the number is invalid */public void set(String s) throws NumberFormatException {hi8 = 0;lo8 = 0;final long lastDigit = 0xfl << 60;for (int i = 0; i < s.length(); ++i) {int digit = getHexDigit(s.charAt(i));if ((lastDigit & hi8) != 0) {throw new NumberFormatException(s + " overflowed 16 bytes");}hi8 <<= 4;hi8 |= (lo8 & lastDigit) >>> 60;lo8 <<= 4;lo8 |= digit;}}/** * Set the number to a given long. *  * @param l *            the new value, which is treated as an unsigned number */public void set(long l) {lo8 = l;hi8 = 0;}/** * Map a hexadecimal character into a digit. *  * @param ch *            the character * @return the digit from 0 to 15 * @throws NumberFormatException */private static int getHexDigit(char ch) throws NumberFormatException {if (ch >= '0' && ch <= '9') {return ch - '0';}if (ch >= 'a' && ch <= 'f') {return ch - 'a' + 10;}if (ch >= 'A' && ch <= 'F') {return ch - 'A' + 10;}throw new NumberFormatException(ch + " is not a valid hex digit");}private static final Unsigned16 TEN = new Unsigned16(10);public static Unsigned16 fromDecimal(String s) throws NumberFormatException {Unsigned16 result = new Unsigned16();Unsigned16 tmp = new Unsigned16();for (int i = 0; i < s.length(); i++) {char ch = s.charAt(i);if (ch < '0' || ch > '9') {throw new NumberFormatException(ch+ " not a valid decimal digit");}int digit = ch - '0';result.multiply(TEN);tmp.set(digit);result.add(tmp);}return result;}/** * Return the number as a hex string. */public String toString() {if (hi8 == 0) {return Long.toHexString(lo8);} else {StringBuilder result = new StringBuilder();result.append(Long.toHexString(hi8));String loString = Long.toHexString(lo8);for (int i = loString.length(); i < 16; ++i) {result.append('0');}result.append(loString);return result.toString();}}/** * Get a given byte from the number. *  * @param b *            the byte to get with 0 meaning the most significant byte * @return the byte or 0 if b is outside of 0..15 */public byte getByte(int b) {if (b >= 0 && b < 16) {if (b < 8) {return (byte) (hi8 >> (56 - 8 * b));} else {return (byte) (lo8 >> (120 - 8 * b));}}return 0;}/** * Get the hexadecimal digit at the given position. *  * @param p *            the digit position to get with 0 meaning the most significant * @return the character or '0' if p is outside of 0..31 */public char getHexDigit(int p) {byte digit = getByte(p / 2);if (p % 2 == 0) {digit >>>= 4;}digit &= 0xf;if (digit < 10) {return (char) ('0' + digit);} else {return (char) ('A' + digit - 10);}}/** * Get the high 8 bytes as a long. */public long getHigh8() {return hi8;}/** * Get the low 8 bytes as a long. */public long getLow8() {return lo8;}/** * Multiple the current number by a 16 byte unsigned integer. Overflow is * not detected and the result is the low 16 bytes of the result. The * numbers are divided into 32 and 31 bit chunks so that the product of two * chucks fits in the unsigned 63 bits of a long. *  * @param b *            the other number */void multiply(Unsigned16 b) {// divide the left into 4 32 bit chunkslong[] left = new long[4];left[0] = lo8 & 0xffffffffl;left[1] = lo8 >>> 32;left[2] = hi8 & 0xffffffffl;left[3] = hi8 >>> 32;// divide the right into 5 31 bit chunkslong[] right = new long[5];right[0] = b.lo8 & 0x7fffffffl;right[1] = (b.lo8 >>> 31) & 0x7fffffffl;right[2] = (b.lo8 >>> 62) + ((b.hi8 & 0x1fffffffl) << 2);right[3] = (b.hi8 >>> 29) & 0x7fffffffl;right[4] = (b.hi8 >>> 60);// clear the cur valueset(0);Unsigned16 tmp = new Unsigned16();for (int l = 0; l < 4; ++l) {for (int r = 0; r < 5; ++r) {long prod = left[l] * right[r];if (prod != 0) {int off = l * 32 + r * 31;tmp.set(prod);tmp.shiftLeft(off);add(tmp);}}}}/** * Add the given number into the current number. *  * @param b *            the other number */public void add(Unsigned16 b) {long sumHi;long sumLo;long reshibit, hibit0, hibit1;sumHi = hi8 + b.hi8;hibit0 = (lo8 & 0x8000000000000000L);hibit1 = (b.lo8 & 0x8000000000000000L);sumLo = lo8 + b.lo8;reshibit = (sumLo & 0x8000000000000000L);if ((hibit0 & hibit1) != 0 | ((hibit0 ^ hibit1) != 0 && reshibit == 0))sumHi++; /* add carry bit */hi8 = sumHi;lo8 = sumLo;}/** * Shift the number a given number of bit positions. The number is the low * order bits of the result. *  * @param bits *            the bit positions to shift by */public void shiftLeft(int bits) {if (bits != 0) {if (bits < 64) {hi8 <<= bits;hi8 |= (lo8 >>> (64 - bits));lo8 <<= bits;} else if (bits < 128) {hi8 = lo8 << (bits - 64);lo8 = 0;} else {hi8 = 0;lo8 = 0;}}}@Overridepublic void readFields(DataInput in) throws IOException {hi8 = in.readLong();lo8 = in.readLong();}@Overridepublic void write(DataOutput out) throws IOException {out.writeLong(hi8);out.writeLong(lo8);}}