使用命令行编译打包运行自己的MapReduce程序 Hadoop2.6.0

Hadoop (2.0万) 2016-08-20 14:50:35

网上的 MapReduce WordCount 教程对于如何编译 WordCount.java 几乎是一笔带过… 而有写到的,大多又是 0.20 等旧版本版本的做法,即 javac -classpath /usr/local/hadoop/hadoop-1.0.1/hadoop-core-1.0.1.jar WordCount.java,但较新的 2.X 版本中,已经没有 hadoop-core*.jar 这个文件,因此编辑和打包自己的 MapReduce 程序与旧版本有所不同。

本文以 Hadoop 2.6.0 环境下的 WordCount 实例来介绍 2.x 版本中如何编辑自己的 MapReduce 程序。

Hadoop 2.x 版本中的依赖 jar

Hadoop 2.x 版本中 jar 不再集中在一个 hadoop-core*.jar 中,而是分成多个 jar,如使用 Hadoop 2.6.0 运行 WordCount 实例至少需要如下三个 jar:

  • $HADOOP_HOME/share/hadoop/common/hadoop-common-2.6.0.jar
  • $HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-client-core-2.6.0.jar
  • $HADOOP_HOME/share/hadoop/common/lib/commons-cli-1.2.jar

实际上,通过命令 hadoop classpath 我们可以得到运行 Hadoop 程序所需的全部 classpath 信息。

编译、打包 Hadoop MapReduce 程序

我们将 Hadoop 的 classhpath 信息添加到 CLASSPATH 变量中,在 ~/.bashrc 中增加如下几行:

export HADOOP_HOME=/usr/local/hadoop
export CLASSPATH=$($HADOOP_HOME/bin/hadoop classpath):$CLASSPATH

别忘了执行 source ~/.bashrc 使变量生效,接着就可以通过 javac 命令编译 WordCount.java 了(使用的是 Hadoop 源码中的 WordCount.java,源码在文本最后面):

  1. javac WordCount.java
Shell 命令

编译时会有警告,可以忽略。编译后可以看到生成了几个 .class 文件。

使用命令行编译打包运行自己的MapReduce程序 Hadoop2.6.0 (https://www.tiejiang.org/) Hadoop 第1张使用Javac编译自己的MapReduce程序

接着把 .class 文件打包成 jar,才能在 Hadoop 中运行:

  1. jar -cvf WordCount.jar ./WordCount*.class
Shell 命令

打包完成后,运行试试,创建几个输入文件:

  1. mkdir input
  2. echo "echo of the rainbow" > ./input/file0
  3. echo "the waiting game" > ./input/file1
Shell 命令

使用命令行编译打包运行自己的MapReduce程序 Hadoop2.6.0 (https://www.tiejiang.org/) Hadoop 第2张创建WordCount的输入

开始运行:

  1. /usr/local/hadoop/bin/hadoop jar WordCount.jar WordCount input output
Shell 命令

不过这边可能会遇到如下的提示 Exception in thread "main" java.lang.NoClassDefFoundError: WordCount

使用命令行编译打包运行自己的MapReduce程序 Hadoop2.6.0 (https://www.tiejiang.org/) Hadoop 第3张提示找不到 WordCount 类

因为程序中声明了 package ,所以在命令中也要 org.apache.hadoop.examples 写完整:

  1. /usr/local/hadoop/bin/hadoop jar WordCount.jar org.apache.hadoop.examples.WordCount input output
Shell 命令

正确运行后的结果如下:

使用命令行编译打包运行自己的MapReduce程序 Hadoop2.6.0 (https://www.tiejiang.org/) Hadoop 第4张WordCount 运行结果

进阶:使用 Eclipse 编译运行 MapReduce 程序

使用命令行编译运行MapReduce程序毕竟有些麻烦,修改一次就得手动编译、打包一次,使用Eclipse编译运行MapReduce程序会更加方便。

WordCount.java 源码

文件位于 hadoop-2.6.0-src\hadoop-mapreduce-project\hadoop-mapreduce-examples\src\main\java\org\apache\hadoop\examples 中:

  1. /**
  2. * Licensed to the Apache Software Foundation (ASF) under one
  3. * or more contributor license agreements. See the NOTICE file
  4. * distributed with this work for additional information
  5. * regarding copyright ownership. The ASF licenses this file
  6. * to you under the Apache License, Version 2.0 (the
  7. * "License"); you may not use this file except in compliance
  8. * with the License. You may obtain a copy of the License at
  9. *
  10. * http://www.apache.org/licenses/LICENSE-2.0
  11. *
  12. * Unless required by applicable law or agreed to in writing, software
  13. * distributed under the License is distributed on an "AS IS" BASIS,
  14. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  15. * See the License for the specific language governing permissions and
  16. * limitations under the License.
  17. */
  18. package org.apache.hadoop.examples;
  19.  
  20. import java.io.IOException;
  21. import java.util.StringTokenizer;
  22.  
  23. import org.apache.hadoop.conf.Configuration;
  24. import org.apache.hadoop.fs.Path;
  25. import org.apache.hadoop.io.IntWritable;
  26. import org.apache.hadoop.io.Text;
  27. import org.apache.hadoop.mapreduce.Job;
  28. import org.apache.hadoop.mapreduce.Mapper;
  29. import org.apache.hadoop.mapreduce.Reducer;
  30. import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
  31. import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
  32. import org.apache.hadoop.util.GenericOptionsParser;
  33.  
  34. public class WordCount {
  35.  
  36. public static class TokenizerMapper
  37. extends Mapper<Object, Text, Text, IntWritable>{
  38.  
  39. private final static IntWritable one = new IntWritable(1);
  40. private Text word = new Text();
  41.  
  42. public void map(Object key, Text value, Context context
  43. ) throws IOException, InterruptedException {
  44. StringTokenizer itr = new StringTokenizer(value.toString());
  45. while (itr.hasMoreTokens()) {
  46. word.set(itr.nextToken());
  47. context.write(word, one);
  48. }
  49. }
  50. }
  51.  
  52. public static class IntSumReducer
  53. extends Reducer<Text,IntWritable,Text,IntWritable> {
  54. private IntWritable result = new IntWritable();
  55.  
  56. public void reduce(Text key, Iterable<IntWritable> values,
  57. Context context
  58. ) throws IOException, InterruptedException {
  59. int sum = 0;
  60. for (IntWritable val : values) {
  61. sum += val.get();
  62. }
  63. result.set(sum);
  64. context.write(key, result);
  65. }
  66. }
  67.  
  68. public static void main(String[] args) throws Exception {
  69. Configuration conf = new Configuration();
  70. String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
  71. if (otherArgs.length != 2) {
  72. System.err.println("Usage: wordcount <in> <out>");
  73. System.exit(2);
  74. }
  75. Job job = new Job(conf, "word count");
  76. job.setJarByClass(WordCount.class);
  77. job.setMapperClass(TokenizerMapper.class);
  78. job.setCombinerClass(IntSumReducer.class);
  79. job.setReducerClass(IntSumReducer.class);
  80. job.setOutputKeyClass(Text.class);
  81. job.setOutputValueClass(IntWritable.class);
  82. FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
  83. FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
  84. System.exit(job.waitForCompletion(true) ? 0 : 1);
  85. }
  86. }
Java

参考资料

  • http://blog.sina.com.cn/s/blog_68cceb610101r6tg.html
  • http://www.cppblog.com/humanchao/archive/2014/05/27/207118.aspx
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