Ubuntu中使用终端运行Hadoop程序

接上一篇《Ubuntu Kylin系统下安装Hadoop2.6.0》

通过上一篇，Hadoop伪分布式基本配好了。

下一步是运行一个MapReduce程序，以WordCount为例：

1. 构建实现类：

cd /usr/local/hadoop
mkdir workspace
cd workspace
gedit WordCount.java

将代码复制粘贴。

import java.io.IOException;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class WordCount {

  public static class TokenizerMapper
       extends Mapper<Object, Text, Text, IntWritable>{

    private final static IntWritable one = new IntWritable();
    private Text word = new Text();

    public void map(Object key, Text value, Context context
                    ) throws IOException, InterruptedException {
      StringTokenizer itr = new StringTokenizer(value.toString());
      while (itr.hasMoreTokens()) {
        word.set(itr.nextToken());
        context.write(word, one);
      }
    }
  }

  public static class IntSumReducer
       extends Reducer<Text,IntWritable,Text,IntWritable> {
    private IntWritable result = new IntWritable();

    public void reduce(Text key, Iterable<IntWritable> values,
                       Context context
                       ) throws IOException, InterruptedException {
      int sum = ;
      for (IntWritable val : values) {
        sum += val.get();
      }
      result.set(sum);
      context.write(key, result);
    }
  }

  public static void main(String[] args) throws Exception {
    Configuration conf = new Configuration();
    Job job = Job.getInstance(conf, "word count");
    job.setJarByClass(WordCount.class);
    job.setMapperClass(TokenizerMapper.class);
    job.setCombinerClass(IntSumReducer.class);
    job.setReducerClass(IntSumReducer.class);
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(IntWritable.class);
    FileInputFormat.addInputPath(job, new Path(args[]));
    FileOutputFormat.setOutputPath(job, new Path(args[]));
    System.exit(job.waitForCompletion(true) ?  : );
  }
}

对于代码的具体分析，下一篇再详细讲解。

2. 编译

(1) 添加JAVA_HOME

　　export JAVA_HOME=/usr/lib/jvm/java-8u5-sun

　　忘记JAVA_HOME的可以使用：

　　echo $JAVA_HOME

(2) 将jdk目录下的bin文件夹添加到环境变量

export PATH=$JAVA_HOME/bin:$PATH

(3) 将hadoop_classpath添加到环境变量

export HADOOP_CLASSPATH=$JAVA_HOME/lib/tools.jar

编译WordCount.java文件

../bin/hadoop com.sun.tools.javac.Main WordCount.java

　　其中com.sun.tools.javac.Main是生成一个编译器的实例

　　上述语句生成三个class: WordCount.class  Reducer.class  TokenizerMapper.class

将上述三个class打包成.jar包

jar cf WordCount.jar WordCount*.class

生成WordCount.jar

3. 运行

bin/hdfs dfs -mkdir /user
bin/hdfs dfs -mkdir /user/hadoop

　　构造输入文件：

bin/hdfs dfs -put etc/hadoop /input

　　其中，etc/hadoop是输入文件，可替换为其他文件

bin/hadoop jar /usr/local/hadoop/workspace/WordCount.jar /input /output

　　查看运行结果

bin/hdfs dfs -cat /output/*

4. 结束Hadoop

sbin/stop-dfs.sh