Java實現MapReduce的方法是使用Hadoop框架。Hadoop是一個開源的分布式計算框架，其中包含了MapReduce編程模型。

在Java中實現MapReduce，主要步驟如下：

1. 編寫Mapper類：實現Map函數，將輸入數據映射為中間鍵值對。

2. 編寫Reducer類：實現Reduce函數，將中間鍵值對按照鍵進行分組并合并。

3. 創建Job對象：設置作業的輸入路徑、輸出路徑、Mapper和Reducer類等信息。

4. 設置Job的輸入數據格式和輸出數據格式。

5. 提交Job并等待任務完成。

具體代碼示例：

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);
}
}

以上是一個經典的Word Count示例，其中TokenizeMapper類實現了Map函數，將輸入的文本進行分詞，并輸出中間鍵值對；IntSumReducer類實現了Reduce函數，對相同鍵的值進行求和；main函數創建了一個Job對象，并設置了輸入路徑、輸出路徑、Mapper和Reducer類等信息，最后提交任務并等待執行結果。