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大数据实训(三)——MapReduce编程实例:词频统计

#MapReduce#YARN#hdfs#IDEA#JDK1.8

实验三:Mapreduce词频统计

3.1启动hadoop服务,输入命令:

start-all.sh

3.2在export目录下,创建wordcount目录,在里面创建words.txt文件,向words.txt输入下面内容。

[root@bogon~]# mkdir -p /export/wordcount
[root@bogon~]# cd /export/wordcount/
[root@bogon~]# vi words.txt
[root@bogon~]# cat words.txt

3.3编辑结束,****上传文件到HDFS指定目录

创建/wordcount/input目录,执行命令:

hdfs dfs -mkdir -p /wordcount/input

3.4将在本地/export/wordcount/目录下的words.txt文件,上传到HDFS的/wordcount/input目录,输入命令:

hdfs dfs -put /export/wordcount/words.txt /wordcount/input

在Hadoop WebUI界面查看目录是否创建成功

3.5使用IDEA创建Maven项目MRWordCount

在pom.xml文件里添加hadoop和junit依赖,内容为:

<dependencies>                                   
    <!--hadoop客户端-->                             
    <dependency>                                 
        <groupId>org.apache.hadoop</groupId>     
        <artifactId>hadoop-client</artifactId>   
        <version>3.3.4</version>                 
    </dependency>                                
    <!--单元测试框架-->                                
    <dependency>                                 
        <groupId>junit</groupId>                 
        <artifactId>junit</artifactId>           
        <version>4.13.2</version>                
    </dependency>                                
</dependencies>                   

3.6****创建日志文件:在resources目录里创建log4j.properties文件

log4j.rootLogger=ERROR, stdout, logfile
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/wordcount.log
log4j.appender.logfile.layout=org.apache.log4j.PatternLayout
log4j.appender.logfile.layout.ConversionPattern=%d %p [%c] - %m%n

3.7****创建词频统计映射器类

(1)创建net.army.mr包,在弹出的new package对话框中输入net.army.mr

(2)在net.army.mr包下创建WordCountMapper类

package net.army.mr;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.io.Text;
import java.io.IOException;
/**
 * 功能:词频统计映射器类
 */
public class WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
    @Override
    protected void map(LongWritable key, Text value, Context context)
            throws IOException, InterruptedException {
        // 获取行内容
        String line = value.toString();
        // 按空格拆分成单词数组
        String[] words = line.split(" ");
        // 遍历单词数组,生成输出键值对
        for (String word : words) {
            context.write(new Text(word), new IntWritable(1));
        }
    }
}

3.8创建WordCountReducer类

package net.army.mr;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
import java.io.IOException;
/**
 * 功能:词频统计归并类
 */
public class WordCountReducer extends Reducer<Text, IntWritable, Text, NullWritable> {
    @Override
    protected void reduce(Text key, Iterable<IntWritable> values, Context context)
            throws IOException, InterruptedException {
        // 定义键(单词)出现次数
        int count = 0;
        // 遍历输入值迭代器
        for (IntWritable value : values) {
            count = count + value.get(); // 针对此案例,可以写为count++;
        }
        // 生成新的键,格式为(word,count)
        String newKey = "(" + key.toString() + "," + count + ")";
        // 输出新的键值对
        context.write(new Text(newKey), NullWritable.get());
    }
}

3.9创建WordCountDriver类

package net.army.mr;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FSDataInputStream;
import org.apache.hadoop.fs.FileStatus;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IOUtils;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.NullWritable;
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.net.URI;
/**
 * 功能:词频统计驱动器类
 */
public class WordCountDriver {
    public static void main(String[] args) throws Exception {
        // 创建配置对象
        Configuration conf = new Configuration();
        // 设置客户端使用数据节点主机名属性
        conf.set("dfs.client.use.datanode.hostname", "true");

        // 获取作业实例
        Job job = Job.getInstance(conf);
        // 设置作业启动类
        job.setJarByClass(WordCountDriver.class);

        // 设置Mapper类
        job.setMapperClass(WordCountMapper.class);
        // 设置map任务输出键类型
        job.setMapOutputKeyClass(Text.class);
        // 设置map任务输出值类型
        job.setMapOutputValueClass(IntWritable.class);

        // 设置Reducer类
        job.setReducerClass(WordCountReducer.class);
        // 设置reduce任务输出键类型
        job.setOutputKeyClass(Text.class);
        // 设置reduce任务输出值类型
        job.setOutputValueClass(NullWritable.class);

        // 定义uri字符串
        String uri = "hdfs://bogon:9000";

        // 创建输入目录
        Path inputPath = new Path(uri + "/wordcount/input");
        // 创建输出目录
        Path outputPath = new Path(uri + "/wordcount/output");

        // 获取文件系统
        FileSystem fs = FileSystem.get(new URI(uri), conf);
        // 删除输出目录(第二个参数设置是否递归)
        fs.delete(outputPath, true);

        // 给作业添加输入目录(允许多个)
        FileInputFormat.addInputPath(job, inputPath);
        // 给作业设置输出目录(只能一个)
        FileOutputFormat.setOutputPath(job, outputPath);

        // 等待作业完成
        job.waitForCompletion(true);

        // 输出统计结果
        System.out.println("======统计结果======");
        FileStatus[] fileStatuses = fs.listStatus(outputPath);
        for (int i = 1; i < fileStatuses.length; i++) {
            // 输出结果文件路径
            System.out.println(fileStatuses[i].getPath());
            // 获取文件系统数据字节输入流
            FSDataInputStream in = fs.open(fileStatuses[i].getPath());
            // 将结果文件显示在控制台
            IOUtils.copyBytes(in, System.out, 4096, false);
        }
    }
}

3****.10运行词频统计驱动器类WordCountDriver,查看结果****


本文转载自: https://blog.csdn.net/weixin_74137248/article/details/139130187
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