Java编写Mapreduce程序过程浅析

<dependencies>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>3.3.0</version>
        </dependency>
        <dependency>
            <groupId>junit</groupId>
            <artifactId>junit</artifactId>
            <version>4.12</version>
            <scope>test</scope>
        </dependency>
        <dependency>
            <groupId>org.slf4j</groupId>
            <artifactId>slf4j-api</artifactId>
            <version>1.7.30</version>
        </dependency>
        <dependency>
            <groupId>junit</groupId>
            <artifactId>junit</artifactId>
            <version>3.8.2</version>
        </dependency>
        <dependency>
            <groupId>junit</groupId>
            <artifactId>junit</artifactId>
            <version>4.12</version>
            <scope>compile</scope>
        </dependency>
    </dependencies>

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapReduce.Mapper;
import org.apache.hadoop.util.StringUtils;
import java.io.IOException;
public class WordCountMapper extends Mapper<LongWritable,Text,Text,LongWritable> {
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        //如果当前数据不为空
        if (value!=null){
            //获取每一行的数据
            String line = value.toString();
            //将一行数据根据空格分开
//            String[] words = line.split(" ");
            String[] words = StringUtils.split(line,' ');//hadoop的StringUtils.split方法对大数据来说比Java自带的拥有更好的性能
            //输出键值对
            for (String word : words) {
                context.write(new Text(word),new LongWritable(1));
            }
        }
    }
}

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
import java.io.IOException;
public class WordCountReducer extends Reducer<Text, LongWritable,Text,LongWritable> {
    @Override
    protected void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException {
        //累加单词的数量
        long sum = 0;
        //遍历单词计数数组,将值累加到sum中
        for (LongWritable value : values) {
            sum += value.get();
        }
        //输出每次最终的计数结果
        context.write(key,new LongWritable(sum));
    }
}

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
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;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
public class WordCountRunner extends Configured implements Tool {
    public static void main(String[] args) throws Exception {
        ToolRunner.run(new Configuration(),new WordCountRunner(),args);
    }
    @Override
    public int run(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf, "word count");
        job.setjarByClass(WordCountRunner.class);
        job.setMapperClass(WordCountMapper.class);
        job.setReducerClass(WordCountReducer.class);
        job.setMapOutpuTKEyClass(Text.class);
        job.setMapOutputValueClass(LongWritable.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(LongWritable.class);
        //设置统计文件输入的路径,将命令行的第一个参数作为输入文件的路径
        //读取maven项目下resources目录的文档    
        String path = getClass().getResource("/words.txt").getPath();
        FileInputFormat.setInputPaths(job,path);
        //设置结果数据存放路径,将命令行的第二个参数作为数据的输出路径
        //输出目录必须不存在!!!
        FileOutputFormat.setOutputPath(job,new Path("./output"));
        return job.waitForCompletion(true) ? 0 : 1;
    }
}