使用IDEA实现MapReduce时间戳排序

首先，需要在IDEA中创建一个Maven项目，并添加Hadoop的依赖。然后，创建一个Java类来实现MapReduce任务。

下面是实现时间戳排序的MapReduce任务的代码：

import java.io.IOException;
import java.text.ParseException;
import java.text.SimpleDateFormat;
import java.util.Date;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
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;

public class TimestampSorter {

  public static class TimestampMapper extends Mapper<LongWritable, Text, LongWritable, Text> {
    
    private static SimpleDateFormat sdf = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
    
    @Override
    public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
      String[] fields = value.toString().split(",");
      String timestampStr = fields[0];
      String data = fields[1];
      
      try {
        Date timestamp = sdf.parse(timestampStr);
        context.write(new LongWritable(timestamp.getTime()), new Text(data));
      } catch (ParseException e) {
        e.printStackTrace();
      }
    }
  }
  
  public static class TimestampReducer extends Reducer<LongWritable, Text, LongWritable, Text> {
    
    @Override
    public void reduce(LongWritable key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
      for (Text value : values) {
        context.write(key, value);
      }
    }
  }
  
  public static void main(String[] args) throws Exception {
    Configuration conf = new Configuration();
    Job job = Job.getInstance(conf, "timestamp sort");
    job.setJarByClass(TimestampSorter.class);
    job.setMapperClass(TimestampMapper.class);
    job.setReducerClass(TimestampReducer.class);
    job.setOutputKeyClass(LongWritable.class);
    job.setOutputValueClass(Text.class);
    FileInputFormat.addInputPath(job, new Path(args[0]));
    FileOutputFormat.setOutputPath(job, new Path(args[1]));
    System.exit(job.waitForCompletion(true) ? 0 : 1);
  }
}

在上面的代码中，我们首先定义了一个Mapper类，将每一行数据的时间戳转换为毫秒级别的long类型，作为Map的输出键，原始数据作为Map的输出值。

然后，定义了一个Reducer类，将Map输出的键值对按照键的大小排序，并将排序后的结果输出到文件中。

最后，在main函数中设置了MapReduce任务的各种参数，包括输入路径、输出路径、Mapper类、Reducer类等，并启动任务。