实现Reduce输出结果的全排序
时间: 2024-03-02 20:53:37 浏览: 25
实现Reduce输出结果的全排序,需要在Reducer中进行一定的修改。以下是一个基本的MapReduce全排序的代码示例:
Mapper函数:
```
public static class SortMapper extends Mapper<Object, Text, IntWritable, IntWritable>{
private IntWritable num = new IntWritable();
public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
String line = value.toString();
num.set(Integer.parseInt(line));
context.write(num, new IntWritable(1));
}
}
```
Reducer函数:
```
public static class SortReducer extends Reducer<IntWritable,IntWritable,IntWritable,IntWritable> {
public void reduce(IntWritable key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
for (IntWritable value : values) {
context.write(key, new IntWritable(1));
}
}
}
```
驱动函数:
```
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
Job job = Job.getInstance(conf, "sort");
job.setJarByClass(Sort.class);
job.setMapperClass(SortMapper.class);
job.setReducerClass(SortReducer.class);
job.setOutputKeyClass(IntWritable.class);
job.setOutputValueClass(IntWritable.class);
job.setMapOutputKeyClass(IntWritable.class);
job.setMapOutputValueClass(IntWritable.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
job.setNumReduceTasks(1); // 设置Reduce任务数为1,确保全局排序
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
```
在这个示例中,Mapper函数与之前的示例相同。在Reducer函数中,我们直接输出键值对,因为它们已经按照键进行了排序。在驱动函数中,我们设置了Reduce任务数为1,以确保全局排序。
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