“21天好习惯”第一期-9

MapReduce中级编程实践

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一、实验目的
通过实验掌握基本的MapReduce编程方法；
掌握用MapReduce解决一些常见的数据处理问题，包括数据去重计数、数据排序。

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二、实验平台
操作系统：Linux
Hadoop版本：2.6.0

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三、实验步骤
（一）对访问同一个网站的用户去重计数。
注：文件userurl_20150911中，数据以”\t”隔开，用户手机号为第三列，网站主域为第17列

在map中将文件按列分割，取需要的两列。在reduce中计数

package mapreduce;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FSDataInputStream;
import org.apache.hadoop.fs.FileSystem;
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 java.io.BufferedReader;
import java.io.IOException;
import java.io.InputStreamReader;
import java.util.ArrayList;
import java.util.HashMap;

public class Count{
    public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable> {

        public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
            String str = value.toString();
            if (str != null && !str.equals("")) {
                String[] fa = str.split("\t");
                String per = fa[2], web = fa[16];
                context.write(new Text(per+'\t'+web),new IntWritable(1));
            }
        }
    }

    public static class Reduce extends Reducer<Text, IntWritable, Text, IntWritable> {
        public static HashMap<String, Integer> mp;
        public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
            int sum=0;
            for(IntWritable t:values)
                sum++;
            context.write(key,new IntWritable(sum));
        }
    }

    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();

        Job job = Job.getInstance(conf);
        job.setJarByClass(Count.class);
        job.setMapperClass(TokenizerMapper.class);
        job.setReducerClass(Reduce.class);

        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);

        job.setoutputKeyClass(Text.class);
        job.setoutputValueClass(IntWritable.class);

        job.setInputFormatClass(TextInputFormat.class);

        FileInputFormat.addInputPath(job, new Path("hdfs://hadoop1:8020/dir1/userurl_20150911"));
        FileOutputFormat.setoutputPath(job, new Path("hdfs://hadoop1:8020/dir1/output_4"));
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

（二）对同一个用户不同记录产生的上下行流量求和后进行排序输出。
注：上行流量位于第25列，下行流量位于第26列

在map中将同一个人的第25、26列相加，在reduce中统计

```java
package mapreduce;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FSDataInputStream;
import org.apache.hadoop.fs.FileSystem;
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.io.IntWritable.Comparator;

import org.apache.hadoop.io.WritableComparable;

import java.io.BufferedReader;
import java.io.IOException;
import java.io.InputStreamReader;
import java.util.ArrayList;
import java.util.HashMap;

public class Liuliang {

    public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable> {

        public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
            String str = value.toString();
            if (str != null && !str.equals("")) {
                String[] fa = str.split("\t");
                String per = fa[2], web = fa[16], up = fa[24], down = fa[25];
                int use = Integer.parseInt(up) + Integer.parseInt(down);
                context.write(new Text(per + '\t' + web), new IntWritable(use));
            }
        }
    }

    public static class Reduce extends Reducer<Text, IntWritable, Text, IntWritable> {
        public static HashMap<String, Integer> mp;

        public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
            int sum = 0;
            for (IntWritable t : values) {
                sum = t.get();
                context.write(key, new IntWritable(sum));
            }
        }
    }

    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        FileSystem fs = FileSystem.get(conf);

        Job job = Job.getInstance(conf);
        job.setJarByClass(Liuliang.class);
        job.setMapperClass(TokenizerMapper.class);
        job.setReducerClass(Reduce.class);

        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);

        job.setoutputKeyClass(Text.class);
        job.setoutputValueClass(IntWritable.class);

        job.setInputFormatClass(TextInputFormat.class);
        FileInputFormat.addInputPath(job, new Path("hdfs://hadoop1:8020/dir1/userurl_20150911"));
        FileOutputFormat.setoutputPath(job, new Path("hdfs://hadoop1:8020/dir1/output_5"));
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

四、实验总结及问题
1、学会使用什么做什么事情；
学会使用mapreduce对文件进行按列分割再统计
2、在实验过程中遇到了什么问题？是如何解决的？
3、还有什么问题尚未解决？可能是什么原因导致的。