mapreduce 项目实例

以下是一个简单的MapReduce项目实例，实现了对文本中单词出现次数的统计：

1. 定义自己的三个类：DcMapper，DcReducer，DcDriver

public class DcMapper 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);
        }
    }
}

public class DcReducer 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 = 0;
        for (IntWritable val : values) {
            sum += val.get();
        }
        result.set(sum);
        context.write(key, result);
    }
}

public class DcDriver {
    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf, "word count");
        job.setJarByClass(DcDriver.class);
        job.setMapperClass(DcMapper.class);
        job.setCombinerClass(DcReducer.class);
        job.setReducerClass(DcReducer.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);
    }
}

2. 编写完代码后，将其打包成JAR文件。

3. 将JAR文件上传到Hadoop集群中，并执行以下命令：

hadoop jar wordcount.jar com.xijing.mapreduce.DcDriver /input /output

其中，/input是输入文件路径，/output是输出文件路径。