根据这个文档，用java写一个完整的且一定能跑通的MapReduce程序（Mapper、Reducer、Driver三个类一个都不能少），目的是计算出所有用户在这段时间内的用户跳失率为（只看不买的用户占比）,以及复购率

为了编写Java MapReduce程序来分析`cookie.txt`中的数据，并计算用户跳失率（只访问没有购买的比例）和复购率（重复购买的比例），我们需要设计一个解决方案，该方案能够有效地从给定的数据集中提取相关信息，并通过Mapper处理每行数据，再由Reducer汇总结果以得出最终的比率。以下是一种可能实现的方式：

### Mapper Class (UserActivityMapper)
在Mapper阶段，我们将解析输入文件中的每一行，并将它们转换为键值对的形式。这里我们关心的是用户的浏览(pv)与购买(buy)行为。我们可以使用用户ID作为键，行为类型(pv或buy)作为值的一部分输出。

import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

public class UserActivityMapper extends Mapper<LongWritable, Text, Text, ActivityCounter> {
    private static final IntWritable ONE = new IntWritable(1);
    private Text userKey = new Text();
    private ActivityCounter activityValue = new ActivityCounter();

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        String[] parts = value.toString().split(",");
        if (parts.length >= 3 && "pv".equals(parts[4])) { // Assuming the pattern is id,id,type,date,count,...
            userKey.set(parts[0] + "," + parts[1]);
            activityValue.setAction("pv");
            activityValue.setCount(ONE);
            context.write(userKey, activityValue);
        } else if ("buy".equals(parts[4])) {
            userKey.set(parts[0] + "," + parts[1]);
            activityValue.setAction("buy");
            activityValue.setCount(ONE);
            context.write(userKey, activityValue);
        }
    }
}

### Reducer Class (UserActivityReducer)
Reducer的任务是接收来自Mapper的所有中间结果，并计算每个用户的总浏览次数和购买次数，进而可以推算出跳失率和复购情况。

import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

public class UserActivityReducer extends Reducer<Text, ActivityCounter, Text, ActivitySummary> {

    @Override
    protected void reduce(Text key, Iterable<ActivityCounter> values, Context context) throws IOException, InterruptedException {
        int pvTotal = 0;
        int buyTotal = 0;
        for (ActivityCounter val : values) {
            if ("pv".equals(val.getAction())) {
                pvTotal += val.getCount().get();
            } else if ("buy".equals(val.getAction())) {
                buyTotal += val.getCount().get();
            }
        }
        
        float churnRate = (float)(pvTotal - buyTotal) / pvTotal;
        float repeatPurchaseRate = (float)buyTotal / (buyTotal > 0 ? buyTotal : 1); // Avoid division by zero
        
        ActivitySummary summary = new ActivitySummary(churnRate, repeatPurchaseRate);
        context.write(key, summary);
    }
}

### Driver Class (UserActivityAnalysisJob)
最后，在驱动器中设置作业参数并启动MapReduce任务。

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class UserActivityAnalysisJob {
    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf, "User Activity Analysis");
        job.setJarByClass(UserActivityAnalysisJob.class);
        job.setMapperClass(UserActivityMapper.class);
        job.setReducerClass(UserActivityReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(ActivitySummary.class);

        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

请注意：上述代码示例假设了一些自定义类如`ActivityCounter`, `ActivitySummary`已经定义好，用来存储具体的计数或者比率等信息。实际编码时需要根据具体情况调整这些细节部分。此外，也需要考虑错误处理及异常情况，以上仅为简化版示例。