如何快速利用Hadoop进行MapReduce的WordCount任务？

MapReduce的WordCount快速使用Hadoop

1. 环境准备

确保你已经安装了Hadoop和Java，如果没有，请参考官方文档进行安装：https://hadoop.apache.org/docs/stable/hadoopprojectdist/hadoopcommon/SingleCluster.html

2. 编写MapReduce程序

2.1 编写Mapper类

创建一个名为WordCountMapper.java的文件，并编写如下代码：

import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
public class WordCountMapper extends 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, Context context) throws IOException, InterruptedException {
        String[] words = value.toString().split("\s+");
        for (String w : words) {
            word.set(w);
            context.write(word, one);
        }
    }
}

2.2 编写Reducer类

创建一个名为WordCountReducer.java的文件，并编写如下代码：

import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
public class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
    public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
        int sum = 0;
        for (IntWritable val : values) {
            sum += val.get();
        }
        context.write(key, new IntWritable(sum));
    }
}

2.3 编译打包

将这两个类编译成jar包：

$ javac classpathhadoop classpath d wordcount_classes WordCountMapper.java WordCountReducer.java
$ jar cvf wordcount.jar C wordcount_classes .

3. 运行MapReduce作业

3.1 准备输入数据

将你的文本文件上传到HDFS上的一个目录，例如/input：

$ hdfs dfs mkdir /input
$ hdfs dfs put localfile.txt /input

3.2 运行MapReduce作业

运行以下命令来执行MapReduce作业：

$ hadoop jar wordcount.jar org.example.WordCountDriver /input /output

org.example.WordCountDriver是你的驱动程序类，它应该包含一个main方法来启动作业，你可以在WordCountDriver.java文件中添加以下代码：

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.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class WordCountDriver {
    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf, "word count");
        job.setJarByClass(WordCountDriver.class);
        job.setMapperClass(WordCountMapper.class);
        job.setCombinerClass(WordCountReducer.class);
        job.setReducerClass(WordCountReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

编译并打包这个驱动程序：

$ javac classpathhadoop classpath d driver_classes WordCountDriver.java
$ jar cvf driver.jar C driver_classes .

再次运行MapReduce作业：

$ hadoop jar driver.jar org.example.WordCountDriver /input /output

3.3 查看输出结果

查看HDFS上的输出目录/output：

$ hdfs dfs ls /output
$ hdfs dfs cat /output/partr00000

这将显示单词计数的结果。