Hadoop实战-环境搭建

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Hadoop实战-MapReduce

1. 编写Mapper的代码

#vi WordMap.java

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

//实现Mapper
public class WordMap extends Mapper<LongWritable, Text, Text, LongWritable>{

    @Override
    protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, LongWritable>.Context context)
            throws IOException, InterruptedException {
        //读取到的一行字符串,按照空格分割单词
        String line = value.toString();
        String[] words = line.split(" ");
       
        for (String word : words) {
            //将分割的单词word输出为key，次数输出为value，次数为1，这行数据会输到reduce中，
            context.write(new Text(word), new LongWritable(1));
        }
    }
}

2.编写Reducer的代码

#vi WordReduce.java

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

//实现Reducer
public class WordReduce extends Reducer<Text, LongWritable, Text, LongWritable> {

    @Override
 
 /**
  * 第一个Text: 是传入的单词名称，是Mapper中传入的
  * 第二个：LongWritable 是该单词出现了多少次，这个是mapreduce计算出来的
  * 第三个Text: 是输出单词的名称 ，这里是要输出到文本中的内容
  * 第四个LongWritable： 是输出时显示出现了多少次，这里也是要输出到文本中的内容
  */
    protected void reduce(Text key, Iterable<LongWritable> values,
            Reducer<Text, LongWritable, Text, LongWritable>.Context context) throws IOException, InterruptedException {
       
  //累加统计
  long count = 0;
        for (LongWritable num : values) {
            count += num.get();
        }
        context.write(key, new LongWritable(count));
    }
}

3.编写main方法执行这个MapReduce

#vi WordMapReduce.java

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;

//实现MapReduce
public class WordMapReduce{

    public static void main(String[] args) throws IOException {
        Configuration conf = new Configuration();
        //如果是打包在linux上运行，则不需要写这两行代码
  /*       
  //指定运行在yarn中
        conf.set("mapreduce.framework.name", "yarn");
        //指定resourcemanager的主机名
        conf.set("yarn.resourcemanager.hostname", "localhost");
  */
        Job job = Job.getInstance(conf);
       
        //使得hadoop可以根据类包，找到jar包在哪里
        job.setJarByClass(WordMapReduce.class);
       
        //指定Mapper的类
        job.setMapperClass(WordMap.class);
        //指定reduce的类
        job.setReducerClass(WordReduce.class);
       
        //设置Mapper输出的类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(LongWritable.class);
       
        //设置最终输出的类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(LongWritable.class);
       
        //指定输入文件的位置，这里为了灵活，接收外部参数
        FileInputFormat.setInputPaths(job, new Path(args[0]));
        //指定输入文件的位置，这里接收启动参数
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
       
        //将job中的参数，提交到yarn中运行
        //job.submit();
        try {
            job.waitForCompletion(true);
            //这里的为true,会打印执行结果
        } catch (ClassNotFoundException | InterruptedException e) {
            e.printStackTrace();
        }
    }
}

4. 编译WordMapReduce
注意环境变量
#export CLASSPATH=.:/usr/local/soft/jdk1.8.0_171/lib:/usr/local/soft/jdk1.8.0_171/jre/lib:$(/usr/local/soft/hadoop/bin/hadoop classpath):$CLASSPATH

编译
#javac  WordMap.java
#javac  WordReduce.java
#javac  WordMapReduce.java

5.打包 WordMap、WordReduce、WordMapReduce的class打包
#jar cvf WordMapReduce.jar Word*.class

6.运行WordMapReduce
#hadoop jar WordMapReduce.jar WordMapReduce HdfsInput HdfsOutput

7.查看运行结果
#hadoop fs -ls HdfsOutput
#hadoop fs -cat HdfsOutput/*
#hadoop fs -get HdfsOutput LocalOutput
#cat LocalOutput/*