三十、MapReduce之wordcount案例(环境搭建及案例实施)-阿里云开发者社区

三十、MapReduce之wordcount案例(环境搭建及案例实施)

2022-05-28 209

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简介： 三十、MapReduce之wordcount案例(环境搭建及案例实施)

环境准备：

Hadoop2.6.0

IDEA

maven3.5.4

案例分析：

MapReduce是一种编程模型，用于大规模数据集（大于1TB）的并行运算。它极大地方便了编程人员在不会分布式并行编程的情况下，将自己的程序运行在分布式系统上。本项目用到的便是俗称Helloword的数据提取案例，官网源码见hadoop安装目录：

/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.6.0.jar

注意：在windows下直接查看需要反编译工具，解析jar包

输入数据：

期望输出数据：

环境搭建：

1.配置maven

将下载好的maven路径配置进去

2.配置解释器

3.在pom.xml文件中添加如下依赖

如下依赖只需要更改版本号即可，导入后刷新IDEA即可自动下载依赖

<dependencies>
  <dependency>
    <groupId>junit</groupId>
    <artifactId>junit</artifactId>
    <version>RELEASE</version>
  </dependency>
  <dependency>
    <groupId>org.apache.logging.log4j</groupId>
    <artifactId>log4j-core</artifactId>
    <version>2.8.2</version>
  </dependency>
  <dependency>
    <groupId>org.apache.hadoop</groupId>
    <artifactId>hadoop-common</artifactId>
    <version>2.7.2</version>
  </dependency>
  <dependency>
    <groupId>org.apache.hadoop</groupId>
    <artifactId>hadoop-client</artifactId>
    <version>2.7.2</version>
  </dependency>
  <dependency>
    <groupId>org.apache.hadoop</groupId>
    <artifactId>hadoop-hdfs</artifactId>
    <version>2.7.2</version>
  </dependency>
</dependencies>

4.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
程序编写：

（1）编写Mapper类

package org.example.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> {
    private Text outk = new Text();
    private IntWritable outv = new IntWritable(1);
    @Override
    protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, IntWritable>.Context context) throws IOException, InterruptedException {
        //获取一行
        String line = value.toString();
        //切割
        String[] words = line.split(" ");
        //循环写出
        for (String word : words) {
            //封装
            outk.set(word);
            //写出
            context.write(outk, outv);
        }
    }
}

（2）编写Reducer类

package org.example.mapreduce.wordcount;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
import java.io.IOException;
public class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
    IntWritable outV = new IntWritable();
    @Override
    protected void reduce(Text key, Iterable<IntWritable> values, Reducer<Text, IntWritable, Text, IntWritable>.Context context) throws IOException, InterruptedException {
        int sum = 0;
        //tuomasi(1,1)
        //累加
        for (IntWritable value : values) {
            sum += value.get();
        }
        //写出
        outV.set(sum);
        context.write(key, outV);
    }
}

（3）编写Driver类

package org.example.mapreduce.wordcount;
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;
import java.io.IOException;
public class WordCountDriver {
    public static void main(String[] args) throws IOException, ClassNotFoundException ,InterruptedException{
        //1.获取job
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);
        //2.设置jor包路径
        job.setJarByClass(WordCountDriver.class);
        //3.关联mapper和reducer
        job.setMapperClass(WordCountMapper.class);
        job.setReducerClass(WordCountReducer.class);
        //4.设置mapper输出的k,v类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);
        //5.设置最终输出的K,V类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        //6.设置输入路径和输出路径
        FileInputFormat.setInputPaths(job, new Path("E:\\input\\inputword"));
        FileOutputFormat.setOutputPath(job, new Path("E:\\output\\outputword"));
        //7.提交job作业
        boolean result = job.waitForCompletion(true);
        System.exit(result ? 0 : 1);
    }
}