每日任务

今天做MapReduce实验：

现有一个某电商网站的数据文件，名为buyer_favorite1，记录了用户收藏的商品以及收藏的日期，文件buyer_favorite1中包含（用户id，商品id，收藏日期）三个字段，数据内容以“\t”分割，由于数据很大，所以为了方便统计我们只截取它的一部分数据，内容如下：

 

package exper;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.NullWritable;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;

public class Filter {
    public static class Map extends Mapper<Object, Text, Text, NullWritable> {
        private static Text newKey = new Text();
        public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
            String line = value.toString();
            System.out.println(line);
            String arr[] = line.split("   ");
            newKey.set(arr[1]);
            context.write(newKey, NullWritable.get());
            System.out.println(newKey);
        }
    }

    public static class Reduce extends Reducer<Text, NullWritable, Text, NullWritable> {
        public void reduce(Text key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException {
            context.write(key, NullWritable.get());
        }
    }

    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        Configuration conf = new Configuration();
        System.out.println("start");
        Job job = new Job(conf, "filter");
        job.setJarByClass(Filter.class);
        job.setMapperClass(Map.class);
        job.setReducerClass(Reduce.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(NullWritable.class);
        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputFormatClass(TextOutputFormat.class);
//        Path in = new Path("hdfs://localhost:9000/mapreduce/1in");
//        Path out = new Path("hdfs://localhost:9000/mapreduce/1out");
        FileInputFormat.addInputPath(job,new Path(args[0]));
        FileOutputFormat.setOutputPath(job,new Path(args[1]));
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

 

实验二：

现有某电商关于商品点击情况的数据文件，表名为goods_click，包含两个字段（商品分类，商品点击次数），分隔符“\t”，由于数据很大，所以为了方便统计我们只截取它的一部分数据，

 

package exper;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.NullWritable;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;

public class MyAverage {
    public static class Map extends Mapper<Object, Text, Text, IntWritable> {
        private static Text newKey = new Text();

        public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
            String line = value.toString();
            System.out.println(line);
            String arr[] = line.split("   ");
            newKey.set(arr[0]);
            int click = Integer.parseInt(arr[1]);
            context.write(newKey, new IntWritable(click));
        }
    }

    public static class Reduce extends Reducer<Text, IntWritable, Text, IntWritable> {
        public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
            int num = 0;
            int count = 0;
            for (IntWritable val : values) {
                num += val.get();
                count++;
            }
            int avg = num / count;
            context.write(key, new IntWritable(avg));
        }
    }

    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        Configuration conf = new Configuration();
        System.out.println("start");
        Job job = new Job(conf, "MyAverage");
        job.setJarByClass(MyAverage.class);
        job.setMapperClass(Map.class);
        job.setReducerClass(Reduce.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputFormatClass(TextOutputFormat.class);
//        Path in = new Path("D:\\mapreduce\\2in");
//        Path out = new Path("D:\\mapreduce\\2out");
        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        System.exit(job.waitForCompletion(true) ? 0 : 1);

    }
}

 

实验三：

在电商网站上，当我们进入某电商页面里浏览商品时，就会产生用户对商品访问情况的数据 ，名为goods_visit1，goods_visit1中包含（商品id ，点击次数）两个字段，内容以“\t”分割，由于数据量很大，所以为了方便统计我们只截取它的一部分数据，

 

package exper;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;

public class OneSort {
    public static class Map extends Mapper<Object, Text, IntWritable, Text> {
        private static Text goods = new Text();
        private static IntWritable num = new IntWritable();

        public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
            String line = value.toString();
            String arr[] = line.split("   ");
            num.set(Integer.parseInt(arr[1]));
            goods.set(arr[0]);
            context.write(num, goods);
        }
    }

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

        public void reduce(IntWritable key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
            for (Text val : values) {
                context.write(key, val);
            }
        }
    }

    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        Configuration conf = new Configuration();
        Job job = new Job(conf, "OneSort");
        job.setJarByClass(OneSort.class);
        job.setMapperClass(Map.class);
        job.setReducerClass(Reduce.class);
        job.setOutputKeyClass(IntWritable.class);
        job.setOutputValueClass(Text.class);
        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputFormatClass(TextOutputFormat.class);
//        Path in = new Path("D:\\mapreduce\\3in");
//        Path out = new Path("D:\\mapreduce\\3out");
        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job,new Path(args[1]));
        System.exit(job.waitForCompletion(true) ? 0 : 1);

    }
}