MapReduce的基本内容介绍(附代码)


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1、WordCount程序

1.1 WordCount源程序

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import java.io.IOException;

import java.util.Iterator;

import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;

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.output.FileOutputFormat;

import org.apache.hadoop.util.GenericOptionsParser;

public class WordCount {

    public WordCount() {

    }

     public static void main(String[] args) throws Exception {

        Configuration conf = new Configuration();

        String[] otherArgs = (new GenericOptionsParser(conf, args)).getRemainingArgs();

        if(otherArgs.length < 2) {

            System.err.println("Usage: wordcount <in> [<in>...] <out>");

            System.exit(2);

        }

        Job job = Job.getInstance(conf, "word count");

        job.setJarByClass(WordCount.class);

        job.setMapperClass(WordCount.TokenizerMapper.class);

        job.setCombinerClass(WordCount.IntSumReducer.class);

        job.setReducerClass(WordCount.IntSumReducer.class);

        job.setOutputKeyClass(Text.class);

        job.setOutputValueClass(IntWritable.class);

        for(int i = 0; i < otherArgs.length - 1; ++i) {

            FileInputFormat.addInputPath(job, new Path(otherArgs[i]));

        }

        FileOutputFormat.setOutputPath(job, new Path(otherArgs[otherArgs.length - 1]));

        System.exit(job.waitForCompletion(true)?0:1);

    }

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

        private static final IntWritable one = new IntWritable(1);

        private Text word = new Text();

        public TokenizerMapper() {

        }

        public void map(Object key, Text value, Mapper<Object, Text, Text, IntWritable>.Context context) throws IOException, InterruptedException {

            StringTokenizer itr = new StringTokenizer(value.toString());

            while(itr.hasMoreTokens()) {

                this.word.set(itr.nextToken());

                context.write(this.word, one);

            }

        }

    }

public static class IntSumReducer extends Reducer<Text, IntWritable, Text, IntWritable> {

        private IntWritable result = new IntWritable();

        public IntSumReducer() {

        }

        public void reduce(Text key, Iterable<IntWritable> values, Reducer<Text, IntWritable, Text, IntWritable>.Context context) throws IOException, InterruptedException {

            int sum = 0;

            IntWritable val;

            for(Iterator i$ = values.iterator(); i$.hasNext(); sum += val.get()) {

                val = (IntWritable)i$.next();

            }

            this.result.set(sum);

            context.write(key, this.result);

        }

    }

}

1.2 运行程序,Run As->Java Applicatiion

1.3 编译打包程序,产生Jar文件

2 运行程序

2.1 建立要统计词频的文本文件

wordfile1.txt

Spark Hadoop

Big Data

wordfile2.txt

Spark Hadoop

Big Cloud

2.2 启动hdfs,新建input文件夹,上传词频文件

cd /usr/local/hadoop/

./sbin/start-dfs.sh

./bin/hadoop fs -mkdir input

./bin/hadoop fs -put /home/hadoop/wordfile1.txt input

./bin/hadoop fs -put /home/hadoop/wordfile2.txt input

2.3 查看已上传的词频文件:

hadoop@dblab-VirtualBox:/usr/local/hadoop$ ./bin/hadoop fs -ls .
Found 2 items
drwxr-xr-x - hadoop supergroup 0 2019-02-11 15:40 input
-rw-r--r-- 1 hadoop supergroup 5 2019-02-10 20:22 test.txt
hadoop@dblab-VirtualBox:/usr/local/hadoop$ ./bin/hadoop fs -ls ./input
Found 2 items
-rw-r--r-- 1 hadoop supergroup 27 2019-02-11 15:40 input/wordfile1.txt
-rw-r--r-- 1 hadoop supergroup 29 2019-02-11 15:40 input/wordfile2.txt

2.4 运行WordCount

./bin/hadoop jar /home/hadoop/WordCount.jar input output

屏幕上会输入大段信息

然后可以查看运行结果:

hadoop@dblab-VirtualBox:/usr/local/hadoop$ ./bin/hadoop fs -cat output/*
Hadoop 2
Spark 2

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MapReduce的基本内容介绍(附代码)

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