Java实现MapReduce的方法是使用Hadoop框架。Hadoop是一个开源的分布式计算框架，其中包含了MapReduce编程模型。
在Java中实现MapReduce，主要步骤如下：
编写Mapper类：实现Map函数，将输入数据映射为中间键值对。
编写Reducer类：实现Reduce函数，将中间键值对按照键进行分组并合并。
创建Job对象：设置作业的输入路径、输出路径、Mapper和Reducer类等信息。
设置Job的输入数据格式和输出数据格式。
提交Job并等待任务完成。
具体代码示例：
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.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import java.io.IOException;
import java.util.StringTokenizer;
public class WordCount {
public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable> {
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString());
while (itr.hasMoreTokens()) {
word.set(itr.nextToken());
context.write(word, one);
}
}
}
public static class IntSumReducer 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, "word count");
job.setJarByClass(WordCount.class);
job.setMapperClass(TokenizerMapper.class);
job.setCombinerClass(IntSumReducer.class);
job.setReducerClass(IntSumReducer.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);
}
}
以上是一个经典的Word Count示例，其中TokenizeMapper类实现了Map函数，将输入的文本进行分词，并输出中间键值对；IntSumReducer类实现了Reduce函数，对相同键的值进行求和；main函数创建了一个Job对象，并设置了输入路径、输出路径、Mapper和Reducer类等信息，最后提交任务并等待执行结果。
