本项目有一部分时间戳数据，请编写Mapreduce程序将这些时间戳按升序进行排序，同时转换成对应的date时间

好的，这是一个非常常见的MapReduce任务，我们可以利用MapReduce中的排序功能来完成这个任务。下面是一个示例程序，假设时间戳数据文件的每一行都是一个时间戳，格式为Unix时间戳：

import java.io.IOException;
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.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.mapreduce.lib.partition.HashPartitioner;
import org.apache.hadoop.util.GenericOptionsParser;

public class TimestampSort {

    public static class TimestampMapper extends Mapper<LongWritable, Text, LongWritable, LongWritable> {
        private LongWritable timestamp = new LongWritable();

        public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            long ts = Long.parseLong(value.toString().trim());
            timestamp.set(ts);
            context.write(timestamp, new LongWritable(1));
        }
    }

    public static class TimestampReducer extends Reducer<LongWritable, LongWritable, Text, Text> {
        public void reduce(LongWritable key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException {
            long ts = key.get();
            SimpleDateFormat sdf = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
            String date = sdf.format(new Date(ts * 1000));
            context.write(new Text(date), new Text(""));
        }
    }

    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
        if (otherArgs.length != 2) {
            System.err.println("Usage: timestamps <input> <output>");
            System.exit(2);
        }
        Job job = Job.getInstance(conf, "Timestamp Sort");
        job.setJarByClass(TimestampSort.class);
        job.setMapperClass(TimestampMapper.class);
        job.setReducerClass(TimestampReducer.class);
        job.setPartitionerClass(HashPartitioner.class);
        job.setNumReduceTasks(1);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);
        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputFormatClass(TextOutputFormat.class);
        TextInputFormat.addInputPath(job, new Path(otherArgs[0]));
        TextOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

这个程序的主要思路是将时间戳作为Map的输出key，然后利用MapReduce框架自带的排序功能，将时间戳按升序排序。在Reducer中，将时间戳转换成对应的日期时间格式，输出到文件中即可。由于所有的时间戳都被映射到了同一个Reducer中，所以Reducer也只需要一个，这样可以减少通信开销，提高程序性能。