MapReduce入门实例——WordCount

2022/6/9 23:50:21

编程Tag： import 入门 Apache job MapReduce Hadoop log4j org wordcount

本文主要是介绍MapReduce入门实例——WordCount，对大家解决编程问题具有一定的参考价值，需要的程序猿们随着小编来一起学习吧！

摘要：MapReduce的IDEA配置及WordCount案例

Maven项目配置
- pom.xml
- log4j.properties
编写应用程序
IDEA配置
Debug

Maven项目配置

创建一个空的Maven项目

`pom.xml`

打开根目录下的pom.xml文件，参考配置：

<properties>
    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
    <hadoop.version>3.2.2</hadoop.version>
</properties>
<dependencies>
    <dependency>
        <groupId>junit</groupId>
        <artifactId>junit</artifactId>
        <version>4.10</version>
        <scope>test</scope>
    </dependency>
    <dependency>
        <groupId>org.apache.hadoop</groupId>
        <artifactId>hadoop-client</artifactId>
        <version>${hadoop.version}</version>
    </dependency>
    <dependency>
        <groupId>org.apache.hadoop</groupId>
        <artifactId>hadoop-common</artifactId>
        <version>${hadoop.version}</version>
    </dependency>
    <dependency>
        <groupId>org.apache.hadoop</groupId>
        <artifactId>hadoop-hdfs</artifactId>
        <version>${hadoop.version}</version>
    </dependency>
</dependencies>

`log4j.properties`

在项目的src/main/java/resources下新建log4j.properties，参考配置

# 参考配置1
log4j.rootLogger = info,console 

log4j.appender.console = org.apache.log4j.ConsoleAppender 
log4j.appender.console.Target = System.out 
log4j.appender.console.layout = org.apache.log4j.PatternLayout 
log4j.appender.console.layout.ConversionPattern = %d{ABSOLUTE} %5p %c:%L - %m%n

# glibc lib version diff
log4j.logger.org.apache.hadoop.util.NativeCodeLoader=ERROR

# 参考配置2
log4j.rootLogger = debug,stdout

### 输出信息到控制台 ###
log4j.appender.stdout = org.apache.log4j.ConsoleAppender
log4j.appender.stdout.Target = System.out
log4j.appender.stdout.layout = org.apache.log4j.PatternLayout
log4j.appender.stdout.layout.ConversionPattern = [%-5p] %d{yyyy-MM-dd HH:mm:ss,SSS} method:%l%n%m%n

编写应用程序

/**
 * 导入包
 */

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
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.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import java.io.IOException;

/**
 * WordCount应用程序
 */
public class WordCountApp {
    
    /**
     * Mapper
     */
    public static class MyMapper extends Mapper<LongWritable, Text, Text, LongWritable> {
        LongWritable one = new LongWritable(1);
        @Override
        public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            // 接收到的每一行数据
            String line = value.toString();
            // 按照指定分隔符进行拆分
            String[] words = line.split(" ");
            for(String word: words){
                // 通过上下文把map的处理结果输出
                context.write(new Text(word), one);
            }
        }
    }

    /**
     * Reduce归并
     */
    public static class MyReducer extends Reducer<Text, LongWritable, Text, LongWritable>{
        @Override
        protected void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException{
            long sum = 0;
            for(LongWritable value: values){
                // 求key出现的次数
                sum += value.get();
            }
            // 最终统计结果输出
            context.write(key, new LongWritable(sum));
        }
    }

    /**
     * 定义Driver：封装了MapReduce作业的所有信息
     */
    public static void main(String[] args) throws Exception{
        // windows版本设置HADOOP_HOME环境变量后，若不重启电脑，需要填加该语句
        //System.setProperty("hadoop.home.dir", "C:/Development/hadoop");
        System.setProperty("hadoop.home.dir", "/usr/local/hadoop");
        // 设置操作用户，默认root
        System.setProperty("HADOOP_USER_NAME", "root");
        //创建Configuration
        Configuration configuration = new Configuration();
        // 设置fs.defaultFS参数，默认本地读取
        configuration.set("fs.defaultFS", "hdfs://master:9000");
        // 若参数数量不为2，报错退出，第一个参数读取是输入目录（HDFS），第二个参数是输出目录
        if (args.length != 2) {
            System.err.println("Usage: MyDriver <in> <out>");
            System.exit(2);
        }
        // 如果输出目录存在，则删除
        Path mypath = new Path(args[1]);
        FileSystem hdfs = mypath.getFileSystem(configuration);
        if (hdfs.isDirectory(mypath)) {
            hdfs.delete(mypath, true);
        }
        //创建Job
        Job job = Job.getInstance(configuration, "wordcount");
        //设置job的处理类
        job.setJarByClass(WordCountApp.class);
        //设置作业处理的输入路径
        FileInputFormat.setInputPaths(job, new Path(args[0]));
        //设置map相关参数
        job.setMapperClass(MyMapper.class);
        //设置Map阶段的输出类型: k2 和V2的类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(LongWritable.class);
        //分区，排序，规约，分组步骤采用默认方式
        //设置reduce相关参数
        job.setReducerClass(MyReducer.class);
        //设置Reduce阶段的输出类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(LongWritable.class);
        //设置作业处理的输出路径
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}