<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 {
        //如果當(dāng)前數(shù)據(jù)不為空
        if (value!=null){
            //獲取每一行的數(shù)據(jù)
            String line = value.toString();
            //將一行數(shù)據(jù)根據(jù)空格分開(kāi)
//            String[] words = line.split(" ");
            String[] words = StringUtils.split(line,' ');//hadoop的StringUtils.split方法對(duì)大數(shù)據(jù)來(lái)說(shuō)比Java自帶的擁有更好的性能
            //輸出鍵值對(duì)
            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 {
        //累加單詞的數(shù)量
        long sum = 0;
        //遍歷單詞計(jì)數(shù)數(shù)組,將值累加到sum中
        for (LongWritable value : values) {
            sum += value.get();
        }
        //輸出每次最終的計(jì)數(shù)結(jié)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);
        //設(shè)置統(tǒng)計(jì)文件輸入的路徑,將命令行的第一個(gè)參數(shù)作為輸入文件的路徑
        //讀取maven項(xiàng)目下resources目錄的文檔    
        String path = getClass().getResource("/words.txt").getPath();
        FileInputFormat.setInputPaths(job,path);
        //設(shè)置結(jié)果數(shù)據(jù)存放路徑,將命令行的第二個(gè)參數(shù)作為數(shù)據(jù)的輸出路徑
        //輸出目錄必須不存在!!!
        FileOutputFormat.setOutputPath(job,new Path("./output"));
        return job.waitForCompletion(true) ? 0 : 1;
    }
}