Ubuntu中使用终端运行Hadoop程序

时间：2021-04-26 16:16:50

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

　　

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