编写mapreduce分区操作代码

在MapReduce中，分区操作是将Mapper产生的键值对按照某种规则分到不同的Reducer中进行处理。下面是一个简单的示例代码，可以将Mapper产生的键值对按照键的首字母分到不同的Reducer中进行处理。

public class PartitionerExample {
    public static class MyMapper 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 static class MyPartitioner extends Partitioner<Text, IntWritable> {
        public int getPartition(Text key, IntWritable value, int numReduceTasks) {
            String firstLetter = key.toString().substring(0, 1);
            if (firstLetter.matches("[a-mA-M]")) {
                return 0;
            } else {
                return 1 % numReduceTasks;
            }
        }
    }

    public static class MyReducer 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 static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf, "partitioner example");
        job.setJarByClass(PartitionerExample.class);
        job.setMapperClass(MyMapper.class);
        job.setPartitionerClass(MyPartitioner.class);
        job.setNumReduceTasks(2);
        job.setReducerClass(MyReducer.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);
    }
}

在上述代码中，我们自定义了一个分区器`MyPartitioner`，它将键值对按照键的首字母进行分区。如果键的首字母是a-m或A-M，那么该键值对将会被分配到第0个Reducer中；否则，该键值对将会被分配到第1个Reducer中。在`MyReducer`中，我们对每个键进行求和操作，并将结果输出。在main函数中，我们指定了输入路径和输出路径，并将分区数设置为2。

需要注意的是，分区操作在MapReduce中是可选的，如果不指定分区器，MapReduce框架会使用默认的哈希分区器将键值对均匀地分配到各个Reducer中进行处理。