根据这个cookie.txt文本使用java的mapreduce，写一段完整的且一定能够跑通的计算pv数量的mapreduce代码

针对您提供的`cookie.txt`文件内容，下面将给出一个Java中基于Hadoop MapReduce框架实现统计PV（页面访问量）的示例代码片段。请注意，在实际部署前需要确保您的环境中已正确配置好Hadoop环境，并且该代码仅作为参考指导之用：

import java.io.IOException;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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 PVCounter {

    public static class TokenizerMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
        private final static IntWritable one = new IntWritable(1);
        private Text word = new Text();

        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            // 假设每行数据格式为 "id,id,...,pv,..."
            String[] parts = value.toString().split(",");
            if (parts.length > 3 && !"pv".equals(parts[3])) {
                word.set(parts[3]);
                context.write(word, one);
            }
        }
    }

    public static class IntSumReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
        private IntWritable result = new IntWritable();

        @Override
        protected 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, "pv counter");
        job.setJarByClass(PVCounter.class);
        job.setMapperClass(TokenizerMapper.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);
    }
}

这段代码定义了一个简单的MapReduce程序来处理输入文件中的每一行记录，并提取出每个用户的PV值进行计数。它首先通过Mapper读取输入文件的每一行并解析，然后在Reducer阶段汇总所有相同的键对应的值。最后输出各个日期下的总PV数。

要运行此程序，请先准备好您的输入目录和输出目录路径，然后替换`main`方法内的`args[0]`与`args[1]`参数即可执行任务。此外还需注意调整项目的依赖以引入正确的Hadoop客户端库版本。