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Hadoop学习笔记——WordCount

时间:2017-03-26 19:36:24      阅读:161      评论:0      收藏:0      [点我收藏+]

标签:运行   dir   删除   []   mapr   except   reduce   test   image   

1.在IDEA下新建工程,选择from Mevan

GroupId:WordCount

ArtifactId:com.hadoop.1st

Project name:WordCount

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2.pom.xml文件

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>

    <groupId>WordCount</groupId>
    <artifactId>com.hadoop.1st</artifactId>
    <version>1.0-SNAPSHOT</version>

    <repositories>
        <repository>
            <id>apache</id>
            <url>http://maven.apache.org</url>
        </repository>
    </repositories>

    <dependencies>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-core</artifactId>
            <version>1.2.1</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-common</artifactId>
            <version>2.7.1</version>
        </dependency>
    </dependencies>

    <build>
        <plugins>
            <plugin>
                <artifactId>maven-dependency-plugin</artifactId>
                <configuration>
                    <excludeTransitive>false</excludeTransitive>
                    <stripVersion>true</stripVersion>
                    <outputDirectory>./lib</outputDirectory>
                </configuration>

            </plugin>
        </plugins>
    </build>
</project>

 3.main/java目录下新建WordCount.java文件

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;

import java.io.IOException;
import java.util.StringTokenizer;


/**
 * Created by common on 17-3-26.
 */
public class WordCount {
    public static class WordCountMap extends
            Mapper<LongWritable, Text, Text, IntWritable> {

        private final IntWritable one = new IntWritable(1);
        private Text word = new Text();

        public void map(LongWritable key, Text value, Context context)
                throws IOException, InterruptedException {
            String line = value.toString();
            StringTokenizer token = new StringTokenizer(line);
            while (token.hasMoreTokens()) {
                word.set(token.nextToken());
                context.write(word, one);
            }
        }
    }

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

        public void reduce(Text key, Iterable<IntWritable> values,
                           Context context) throws IOException, InterruptedException {
            int sum = 0;
            for (IntWritable val : values) {
                sum += val.get();
            }
            context.write(key, new IntWritable(sum));
        }
    }

    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = new Job(conf);
        job.setJarByClass(WordCount.class);
        job.setJobName("wordcount");

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);

        job.setMapperClass(WordCountMap.class);
        job.setReducerClass(WordCountReduce.class);

        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputFormatClass(TextOutputFormat.class);

        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        job.waitForCompletion(true);
    }
}

 4.在src同级目录下新建input目录,以及下面的test.segmented文件

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test.segmented文件内容

aa
bb
cc
dd
aa
cc
ee
ff
ff
gg
hh
aa

4.在run configuration下设置运行方式为Application

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5.运行java文件,将会生成output目录,part-r-00000为运行的结果,下次运行必须删除output目录,否则会报错

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Hadoop学习笔记——WordCount

标签:运行   dir   删除   []   mapr   except   reduce   test   image   

原文地址:http://www.cnblogs.com/tonglin0325/p/6623566.html

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