根据这个文档写一个完整的一定能跑通的MapReduce程序（Mapper、Reduce、Driver一个类都不能少），目的是为了计算付费用户数（第五列）、付费用户占比、商品数（第三列）、类目数（第四列）

要编写一个MapReduce程序来处理您提供的`cookie.txt`文件，并从中提取出付费用户数量、付费用户比例、商品总数和类目总数的信息，我们可以将整个过程分为三个主要部分：Mapper、Reducer 和 Driver 类。这里假设输入数据格式是一致的，每行由逗号分隔且共有七个字段组成。

### Mapper.java:

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

public class CookieMapper extends Mapper<LongWritable, Text, Text, LongWritable> {

    private static final int COL_ITEM = 3 - 1; // 商品ID所在位置 (索引从0开始)
    private static final int COL_CATEGORY = 4 - 1; // 类目ID所在位置
    private static final int ACTION_INDEX = 4 - 1; // 动作类型所在位置
    
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        String[] parts = value.toString().split(",");
        
        if ("buy".equals(parts[ACTION_INDEX])) { // 只统计购买行为
            context.write(new Text("payUser"), new LongWritable(1)); // 计算付费用户数
        }
        context.write(new Text("itemCount"), new LongWritable(Long.parseLong(parts[COL_ITEM]))); // 统计所有出现的商品ID
        context.write(new Text("categoryCount"), new LongWritable(Long.parseLong(parts[COL_CATEGORY]))); // 统计所有出现的类目ID
    }
}

### Reducer.java:

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

public class CookieReducer extends Reducer<Text, LongWritable, Text, LongWritable> {

    @Override
    protected void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException {
        long sum = 0;
        for (LongWritable val : values) {
            sum += val.get();
        }

        if ("payUser".equals(key)) {
            context.write(new Text("totalPayUsers"), new LongWritable(sum));
        } else if ("itemCount".equals(key)) {
            context.write(new Text("totalItems"), new LongWritable(sum));
        } else if ("categoryCount".equals(key)) {
            context.write(new Text("totalCategories"), new LongWritable(sum));
        }
    }
}

### Driver.java:

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.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class CookieAnalysis {

    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf, "Cookie Analysis");
        job.setJarByClass(CookieAnalysis.class);
        job.setMapperClass(CookieMapper.class);
        job.setReducerClass(CookieReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(LongWritable.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，在实际运行上述代码之前，需要将其放入Hadoop环境中并进行相应的配置调整以确保能够正确执行。此外，由于题目中的数据较为简单，因此直接通过计数的方式实现了要求的功能；在真实场景中可能还需要考虑更复杂的数据清洗与错误处理逻辑。