代码实现——MapReduce统计单词出现次数

2022-12-01 08:54:13 浏览数 (1)

需求

对以下txt文档进行单词出现次数统计（txt文档在/Users/lizhengi/test/input/目录下）

代码语言：javascript复制
hadoop take spring
spark hadoop hdfs
mapreduce take Tomcat
tomcat
kafka kafka flume
flume
hive

实现

1、新建Maven工程，pom.xml依赖如下

代码语言：javascript复制
<?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>com.lizhengi</groupId>
    <artifactId>Hadoop-API</artifactId>
    <version>1.0-SNAPSHOT</version>

    <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>3.2.1</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>3.2.1</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-hdfs</artifactId>
            <version>3.2.1</version>
        </dependency>
    </dependencies>


</project>

2、src/main/resources目录下，新建一个文件，命名为“log4j.properties”，添加内容如下

代码语言：javascript复制
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

3、编写Mapper类-WcMapper

代码语言：javascript复制
package com.lizhengi.wordcount;

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

/**
 * @author lizhengi
 * @create 2020-07-20
 */
public class WcMapper 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 拿到传入进来的一行内容，把数据类型转化为String
        String line = value.toString();

        // 2 将这一行内容按照分隔符进行一行内容的切割 切割成一个单词数组
        String[] words = line.split(" ");

        // 3 遍历数组，每出现一个单词  就标记一个数字1  <单词，1>
        for (String word : words) {
            //使用mr程序的上下文context 把mapper阶段处理的数据发送出去
            //作为reduce节点的输入数据
            k.set(word);
            context.write(k, v);
        }
    }
}

4、编写Reducer类-WcReducer

代码语言：javascript复制
package com.lizhengi.wordcount;

import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;


/**
 * @author lizhengi
 * @create 2020-07-20
 */
public class WcReducer 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 定义一个计数器
        sum = 0;



        // 2 遍历一组迭代器，把每一个数量1累加起来就构成了单词的总次数
        for (IntWritable count : values) {
            sum  = count.get();
        }

        // 3 输出最终的结果
        v.set(sum);
        context.write(key,v);
    }
}

5、编写Driver驱动类-WcDriver

代码语言：javascript复制
package com.lizhengi.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;

/**
 * @author lizhengi
 * @create 2020-07-20
 */
public class WcDriver {

    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {

        // 1 获取配置信息以及封装任务
        Configuration configuration = new Configuration();
        Job job = Job.getInstance(configuration);

        // 2 设置jar加载路径
        job.setJarByClass(WcDriver.class);

        // 3 设置map和reduce类
        job.setMapperClass(WcMapper.class);
        job.setReducerClass(WcReducer.class);

        // 4 设置map输出
        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, "/Users/lizhengi/test/input");
        FileOutputFormat.setOutputPath(job, new Path("/Users/lizhengi/test/output"));

        // 7 提交
        boolean result = job.waitForCompletion(true);

        System.exit(result ? 0 : 1);
    }
}

结果

代码语言：javascript复制
[root@carlota1]ls /Users/lizhengi/test/output/
#多了两个文件
_SUCCESS	part-r-00000
代码语言：javascript复制
[root@carlota1 output]cat part-r-00000
flume	2
hadoop	2
hdfs	1
hive	1
kafka	2
mapreduce	1
spark 	1
spring	1
take	2
tomcat		2
xml input log4j resources txt

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