Hadoop（十五）项目考核 WordCount案例

一、需求分析

• 需求：在给定的文本文件中统计输出每一个单词出现的总次数
• SEVENTEEN.txt文本内容如下：

say the name seventeen
hello
we are seventeen
nice to meet you
you
very nice

• 按照MapReduce编程规范，分别编写Mapper，Reducer，Driver

1、Mapper
（1）将MapTask传过来的文本内容先转换成String
（2）根据空格将这一行切分成单词
（3）将单词输出为<单词，1>
2、Reducer
（1）汇总各个key的个数
（2）输出该key的总次数
3、Driver
（1）获取配置信息，获取job对象实例
（2）指定本程序的jar包所在的本地路径
（3）关联Mapper/Reducer业务类
（4）指定Mapper输出数据的kv类型
（5）指定最终输出的数据的kv类型
（6）指定job的输入原始文件所在目录
（7）指定job的输出结果所在目录
（8）提交作业

二、环境准备

1、创建maven工程，MapReduceDemo
2、在pom.xml文件中添加如下依赖

<dependencies>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>3.1.3</version>
        </dependency>
        <dependency>
            <groupId>junit</groupId>
            <artifactId>junit</artifactId>
            <version>4.12</version>
        </dependency>
        <dependency>
            <groupId>org.slf4j</groupId>
            <artifactId>slf4j-log4j12</artifactId>
            <version>1.7.30</version>
        </dependency>
</dependencies>

3、在项目的src/main/resources目录下，新建一个文件，命名为“log4j.properties”，在文件中填入

log4j.rootLogger=INFO, stdout 
log4j.appender.stdout=org.apache.log4j.ConsoleAppender 
log4j.appender.stdout.layout=org.apache.log4j.PatternLayout 
log4j.appender.stdout.layout.ConversionPattern=%d %p [%c] - %m%n 
log4j.appender.logfile=org.apache.log4j.FileAppender 
log4j.appender.logfile.File=target/spring.log 
log4j.appender.logfile.layout=org.apache.log4j.PatternLayout 
log4j.appender.logfile.layout.ConversionPattern=%d %p [%c] - %m%n

4、创建包名：com.user.mapreduce.wordcount

三、编写程序

1、编写Mapper类

package com.user.mapreduce.wordcount;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;

public class WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
    Text k = new Text();
    IntWritable v = new IntWritable(1);
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
// 1 获取一行
        String line = value.toString();
// 2 切割
        String[] words = line.split(" ");
// 3 输出
        for (String word : words) {
            k.set(word);
            context.write(k, v);
        }
    }
}

2、编写Reducer类

package com.user.mapreduce.wordcount;

import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
public class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable>{
    int sum;
    IntWritable v = new IntWritable();
    @Override
    protected void reduce(Text key, Iterable<IntWritable> values,Context context) throws IOException, InterruptedException {
// 1 累加求和
        sum = 0;
        for (IntWritable count : values) {
            sum += count.get();
        }
// 2 输出
        v.set(sum);
        context.write(key,v);
    }
}

3、编写Driver驱动类

package com.user.mapreduce.wordcount;

import java.io.IOException;
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.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class WordCountDriver {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
// 1 获取配置信息以及获取 job 对象
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);
// 2 关联本 Driver 程序的 jar
        job.setJarByClass(WordCountDriver.class);
// 3 关联 Mapper 和 Reducer 的 jar
        job.setMapperClass(WordCountMapper.class);
        job.setReducerClass(WordCountReducer.class);
// 4 设置 Mapper 输出的 kv 类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);
// 5 设置最终输出 kv 类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
// 6 设置输入和输出路径
        FileInputFormat.setInputPaths(job, new Path("C:\\Users\\shi.hongpin\\Desktop\\SEVENTEEN.txt"));
        FileOutputFormat.setOutputPath(job, new Path("D:\\hadoop\\output"));
// 7 提交 job
        boolean result = job.waitForCompletion(true);
        System.exit(result ? 0 : 1);
    }
}

• 打包成jar包，到虚拟机运行，输入输出路径要修改为：

FileInputFormat.setInputPaths(job, new Path(arg[0]));
FileOutputFormat.setOutputPath(job, new Path(arg[1]));

5、本地运行成功后在对应的输出路径能看到输出结果

are	1
hello	1
meet	1
name	1
nice	2
say	1
seventeen	2
the	1
to	1
very	1
we	1
you	2

四、提交到集群测试

1、用maven打jar包，需要添加的打包插件依赖

<build>
        <plugins>
            <plugin>
                <artifactId>maven-compiler-plugin</artifactId>
                <version>3.6.1</version>
                <configuration>
                    <source>1.8</source>
                    <target>1.8</target>
                </configuration>
            </plugin>
            <plugin>
                <artifactId>maven-assembly-plugin</artifactId>
                <configuration>
                    <descriptorRefs>
                        <descriptorRef>>jar-with-dependencies</descriptorRef>
                    </descriptorRefs>
                    <executions>
                        <execution>
                            <id>make-assembly</id>
                            <phase>package</phase>
                            <goals>
                                <goal>single</goal>
                            </goals>
                        </execution>
                    </executions>
                </configuration>
            </plugin>
        </plugins>
    </build>

2、将程序打包成jar包，修改名称为wc.jar，并将其拷贝到Hadoop集群/opt/module/hadoop-3.1.3 路径
3、执行WordCount程序

[user@hadoop102 hadoop-3.1.3]$ hadoop jar wc.jar com.user.mapreduce.wordcount.WordCountDriver /SEVENTEEN.txt /wcoutput5

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