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.BZip2Codec
import org.apache.spark.SparkContext
import 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
* <chris.nyberg@ordinal.com>.
*/
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;
}

@Override
public boolean equals(Object o) {
if (o instanceof Unsigned16) {
Unsigned16 other = (Unsigned16) o;
return other.hi8 == hi8 && other.lo8 == lo8;
}
return false;
}

@Override
public 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 chunks
long[] 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 chunks
long[] 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 value
set(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;
}
}
}

@Override
public void readFields(DataInput in) throws IOException {
hi8 = in.readLong();
lo8 = in.readLong();
}

@Override
public void write(DataOutput out) throws IOException {
out.writeLong(hi8);
out.writeLong(lo8);
}
}