针对这个文件帮我写一份 Hadoop MapReduce词频统计源码

当然可以！以下是一个简单的Hadoop MapReduce程序，用于统计`word.txt`文件中的单词频率。该程序包括一个Mapper类和一个Reducer类，并且使用了Hadoop的基本API。

### Mapper类

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();

    @Override
    public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        String line = value.toString();
        String[] words = line.split(",");
        for (String w : words) {
            word.set(w.trim());
            context.write(word, one);
        }
    }
}

### Reducer类

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> {

    @Override
    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));
    }
}

### 驱动程序

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 {
        if (args.length != 2) {
            System.err.println("Usage: WordCount <input path> <output path>");
            System.exit(-1);
        }

        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf, "Word Count");

        job.setJarByClass(WordCountDriver.class);
        job.setMapperClass(WordCountMapper.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);
    }
}

### 运行步骤

1. **编译代码**：将上述三个类保存到相应的Java文件中（例如`WordCountMapper.java`、`WordCountReducer.java`和`WordCountDriver.java`），然后编译它们。

2. **打包**：将编译后的类打包成一个JAR文件。

3. **运行MapReduce作业**：使用Hadoop命令行工具运行MapReduce作业。假设输入文件在HDFS上的路径是`/input/word.txt`，输出目录为`/output`，则命令如下：

   hadoop jar your-jar-file.jar WordCountDriver /input/word.txt /output

4. **查看结果**：结果将存储在指定的输出目录中，通常是一个名为`part-r-00000`的文件。

希望这对你有帮助！如果有任何问题，请随时提问。