mapreduce编程实现wordcount程序

下面是一个使用MapReduce实现WordCount程序的示例代码：

Mapper代码：

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 line = value.toString();
        StringTokenizer tokenizer = new StringTokenizer(line);
        while (tokenizer.hasMoreTokens()) {
            word.set(tokenizer.nextToken());
            context.write(word, one);
        }
    }
}

Reducer代码：

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));
    }
}

Main函数代码：

public class WordCount {
    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(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);
    }
}

以上代码中，我们首先定义了一个Mapper类和一个Reducer类。Mapper类将输入的文本划分为单词，并将每个单词映射为（单词，1）的键值对；Reducer类对于每个单词的键值对进行合并，计算出每个单词出现的次数。

在Main函数中，我们创建了一个新的MapReduce任务，并指定了输入和输出路径。在任务的配置中，我们指定了Mapper类、Combiner类和Reducer类，并设置了键和值的类型。最后，我们调用waitForCompletion方法来等待任务完成。