运行WordCount案例

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对于如何编译WordCount.java，对于0.20 等旧版本版本的做法很常见，具体如下：

 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，如运行WordCount实例需要如下三个 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

编译、打包 Hadoop MapReduce 程序

将上述 jar 添加至 classpath 路径：

hadoop@ubuntu:~$ export CLASSPATH="$HADOOP_HOME/home/hadoop/opt/hadoop-2.6.0/share/hadoop/common/hadoop-common-2.6.0.jar:$HADOOP_HOME/home/hadoop/opt/hadoop-2.6.0/share/hadoop/mapreduce/hadoop-mapreduce-client-core-2.6.0.jar:$HADOOP_HOME/home/hadoop/opt/hadoop-2.6.0/share/hadoop/common/lib/commons-cli-1.2.jar:$CLASSPATH"

接着就可以编译 WordCount.java 了（使用的是 2.6.0源码中的 WordCount.java）

文件位于/hadoop-2.6.0-src/hadoop-mapreduce-project/hadoop-mapreduce-examples/src/main/java/org/apache/hadoop/examples 中，

javac WordCount.java

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。

/home/hadoop/opt/hadoop-2.6.0/share/hadoop/common/hadoop-common-2.6.0.jar(org/apache/hadoop/fs/Path.class): warning: Cannot find annotation method 'value()' in type 'LimitedPrivate': class file for org.apache.hadoop.classification.InterfaceAudience not found
1 warning
hadoop@ubuntu:~/opt/code$ ls
WordCount.class WordCount.java WordCount$MapClass.class WordCount$Reduce.class

接着把 .class 文件打包成 jar，才能在 Hadoop 中运行：

hadoop@ubuntu:~/opt/code$ jar -cvf WordCount.jar ./WordCount*.class
added manifest
adding: WordCount.class(in = 3363) (out= 1687)(deflated 49%)
adding: WordCount$MapClass.class(in = 1978) (out= 800)(deflated 59%)
adding: WordCount$Reduce.class(in = 1641) (out= 645)(deflated 60%)

创建HDFS所需的输入文件夹：

hadoop@ubuntu:~/opt/code$ mkdir input
hadoop@ubuntu:~/opt/code$ echo "Hello Hadoop Goodbye Hadoop" > ./input/file1
hadoop@ubuntu:~/opt/code$ echo "Hello World Bye World" > ./input/file2
hadoop@ubuntu:~/opt/code$ ls ./input
file1 file2

运行我们的wordcount程序：

hadoop@ubuntu:~$ cd ~/opt/code

hadoop@ubuntu:~/opt/code$ ~/opt/hadoop-2.6.0/bin/hadoop jar WordCount.jar org.apache.hadoop.examples.WordCount input output

程序运行完之后，检查我们的输出结果：

hadoop@ubuntu:~/opt/code$ ls ./outputpart-r-00000  _SUCCESShadoop@ubuntu:~/opt/code$ cat ./output/part-r-00000

Bye 1
Goodbye 1
Hadoop 2
Hello 2
World 2

PS：WordCount.java 源代码如下：

package org.apache.hadoop.mapred;import java.io.IOException;import java.util.ArrayList;import java.util.Iterator;import java.util.List;import java.util.StringTokenizer;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.conf.Configured;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.IntWritable;import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapred.FileInputFormat;import org.apache.hadoop.mapred.FileOutputFormat;import org.apache.hadoop.mapred.JobClient;import org.apache.hadoop.mapred.JobConf;import org.apache.hadoop.mapred.MapReduceBase;import org.apache.hadoop.mapred.Mapper;import org.apache.hadoop.mapred.OutputCollector;import org.apache.hadoop.mapred.Reducer;import org.apache.hadoop.mapred.Reporter;import org.apache.hadoop.util.Tool;import org.apache.hadoop.util.ToolRunner;/** * This is an example Hadoop Map/Reduce application. * It reads the text input files, breaks each line into words * and counts them. The output is a locally sorted list of words and the  * count of how often they occurred. * * To run: bin/hadoop jar build/hadoop-examples.jar wordcount *            [-m <i>maps</i>] [-r <i>reduces</i>] <i>in-dir</i> <i>out-dir</i>  */public class WordCount extends Configured implements Tool {    /**   * Counts the words in each line.   * For each line of input, break the line into words and emit them as   * (<b>word</b>, <b>1</b>).   */  public static class MapClass extends MapReduceBase    implements Mapper<LongWritable, Text, Text, IntWritable> {        private final static IntWritable one = new IntWritable(1);    private Text word = new Text();        public void map(LongWritable key, Text value,                     OutputCollector<Text, IntWritable> output,                     Reporter reporter) throws IOException {      String line = value.toString();      StringTokenizer itr = new StringTokenizer(line);      while (itr.hasMoreTokens()) {        word.set(itr.nextToken());        output.collect(word, one);      }    }  }    /**   * A reducer class that just emits the sum of the input values.   */  public static class Reduce extends MapReduceBase    implements Reducer<Text, IntWritable, Text, IntWritable> {        public void reduce(Text key, Iterator<IntWritable> values,                       OutputCollector<Text, IntWritable> output,                        Reporter reporter) throws IOException {      int sum = 0;      while (values.hasNext()) {        sum += values.next().get();      }      output.collect(key, new IntWritable(sum));    }  }    static int printUsage() {    System.out.println("wordcount [-m <maps>] [-r <reduces>] <input> <output>");    ToolRunner.printGenericCommandUsage(System.out);    return -1;  }    /**   * The main driver for word count map/reduce program.   * Invoke this method to submit the map/reduce job.   * @throws IOException When there is communication problems with the    *                     job tracker.   */  public int run(String[] args) throws Exception {    JobConf conf = new JobConf(getConf(), WordCount.class);    conf.setJobName("wordcount");     // the keys are words (strings)    conf.setOutputKeyClass(Text.class);    // the values are counts (ints)    conf.setOutputValueClass(IntWritable.class);        conf.setMapperClass(MapClass.class);            conf.setCombinerClass(Reduce.class);    conf.setReducerClass(Reduce.class);        List<String> other_args = new ArrayList<String>();    for(int i=0; i < args.length; ++i) {      try {        if ("-m".equals(args[i])) {          conf.setNumMapTasks(Integer.parseInt(args[++i]));        } else if ("-r".equals(args[i])) {          conf.setNumReduceTasks(Integer.parseInt(args[++i]));        } else {          other_args.add(args[i]);        }      } catch (NumberFormatException except) {        System.out.println("ERROR: Integer expected instead of " + args[i]);        return printUsage();      } catch (ArrayIndexOutOfBoundsException except) {        System.out.println("ERROR: Required parameter missing from " +                           args[i-1]);        return printUsage();      }    }    // Make sure there are exactly 2 parameters left.    if (other_args.size() != 2) {      System.out.println("ERROR: Wrong number of parameters: " +                         other_args.size() + " instead of 2.");      return printUsage();    }    FileInputFormat.setInputPaths(conf, other_args.get(0));    FileOutputFormat.setOutputPath(conf, new Path(other_args.get(1)));            JobClient.runJob(conf);    return 0;  }      public static void main(String[] args) throws Exception {    int res = ToolRunner.run(new Configuration(), new WordCount(), args);    System.exit(res);  }}