大数据笔记（七）——Mapreduce程序的开发

一.分析Mapreduce程序开发的流程

1.图示过程

输入：HDFS文件 /input/data.txt

Mapper阶段：

　　　　　　 K1:数据偏移量（以单词记）V1：行数据

                       K2:单词  V2:记一次数

Reducer阶段 :

　　　　　　 K3:单词（=K2） V3:V2计数的集合

                       K4:单词  V4:V3集合中元素累加和

输出：HDFS

2.开发WordCount程序需要的jar

/root/training/hadoop-2.7.3/share/hadoop/common
/root/training/hadoop-2.7.3/share/hadoop/common/lib
       
/root/training/hadoop-2.7.3/share/hadoop/mapreduce
/root/training/hadoop-2.7.3/share/hadoop/mapreduce/lib

3.WordCountMapper.java

package demo.wc;

import java.io.IOException;

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

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

    @Override
    protected void map(LongWritable k1, Text v1, Context context)
            throws IOException, InterruptedException {
        
        //Context代表Mapper的上下文 上文：HDFS 下文：Mapper
        //取出数据： I love beijing
        String data = v1.toString();
        
        //分词
        String[] words = data.split(" ");
        
        //输出K2 V2
        for (String w : words) {
            context.write(new Text(w), new LongWritable(1));
        }
        
    }
    
}

4.WordCountReducer.java

package demo.wc;

import java.io.IOException;

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

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

    @Override
    protected void reduce(Text k3, Iterable<LongWritable> v3,
            Context context) throws IOException, InterruptedException {
        //context 代表Reduce的上下文 上文：Mapper 下文：HDFS
        long total = 0;
        for (LongWritable l : v3) {
            //对v3求和
            total = total + l.get();
        }
        
        //输出K4 V4
        context.write(k3, new LongWritable(total));
    }
    
}

5.WordCountMain.java

package demo.wc;

import java.io.IOException;

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 WordCountMain {

    public static void main(String[] args) throws Exception {
        //创建一个job = mapper + reducer
        Job job = Job.getInstance(new Configuration());
        //ָ指定任务的入口
        job.setJarByClass(WordCountMain.class);
    
        //ָ指定任务的mapper和输出的数据类型
        job.setMapperClass(WordCountMapper.class);
        job.setMapOutputKeyClass(Text.class);//指定k2
        job.setMapOutputValueClass(LongWritable.class);//指定v2
        //ָ指定任务的reducer和输出的数据类型
        job.setReducerClass(WordCountReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(LongWritable.class);
        //ָ指定输入的路径(map)、输出的路径(reduce)
        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        //ִ执行任务
        job.waitForCompletion(true);
    }        
}

打包，传到HDFS上：

运行任务：

hadoop jar wc.jar /input/data.txt /output/day0228/wc

日志信息：

18/03/01 00:14:00 INFO client.RMProxy: Connecting to ResourceManager at bigdata11/192.168.153.11:8032
18/03/01 00:14:01 WARN mapreduce.JobResourceUploader: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
18/03/01 00:14:01 INFO input.FileInputFormat: Total input paths to process : 1
18/03/01 00:14:01 INFO mapreduce.JobSubmitter: number of splits:1
18/03/01 00:14:02 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1519833888534_0001
18/03/01 00:14:02 INFO impl.YarnClientImpl: Submitted application application_1519833888534_0001
18/03/01 00:14:02 INFO mapreduce.Job: The url to track the job: http://bigdata11:8088/proxy/application_1519833888534_0001/
18/03/01 00:14:02 INFO mapreduce.Job: Running job: job_1519833888534_0001
18/03/01 00:14:16 INFO mapreduce.Job: Job job_1519833888534_0001 running in uber mode : false
18/03/01 00:14:16 INFO mapreduce.Job:  map 0% reduce 0%
18/03/01 00:14:24 INFO mapreduce.Job:  map 100% reduce 0%
18/03/01 00:14:31 INFO mapreduce.Job:  map 100% reduce 100%

查看结果：

 hdfs dfs -ls /output/day0228/wc

hdfs dfs -cat /output/day0228/wc/part-r-00000

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