网站建设利益搜索引擎优化的重要性

一、源代码

1. WordCountMapper类

package org.example.wordcounttemplate;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> {//新建输出文本对象(输出的key类型)private Text text = new Text();//新建输出IntWritable对象(输出的value类型)private IntWritable intWritable = new IntWritable( 1);/*** 重写map方法* @param key 文本的索引* @param value 文本值* @param context 上下文对象* @throws IOException* @throws InterruptedException*/@Overrideprotected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {//获取拆分后的一行文本//mysql mysql value value valueString line = value.toString();//根据分隔符进行单词拆分String[] words = line.split( " ");//循环创建键值对for (String word : words){//输出key值设置text.set (word) ;//进行map输出//igeek igeek -> <igeek ,1> <igeek,1>context.write(text,intWritable);}}
}

2. WordCountReducer类

package org.example.wordcounttemplate;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> {//输出value对象private IntWritable valueOut = new IntWritable();/*** 重写reduce方法* @param key 单词值* @param values 单词出现的次数集合* @param context   上下文对象* @throws IOException* @throws InterruptedException*/@Overrideprotected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {//每个单词出现的次数int sum= 0;//<igeek,(1,1)>for (IntWritable value : values){//累计单词出现的数量sum += value.get();}//进行封装valueOut.set(sum);// reduce输出context.write(key, valueOut);}
}

3. WordCountDriver类

package org.example.wordcounttemplate;
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;/*** 充当mapreduce任务的客户端，用于提交任务*/public class WordCountDriver {public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
//        1.获取配置信息，获取job对象实例Configuration conf=new Configuration();Job job=Job.getInstance(conf);//        2.关联本Driver得jar路径job.setJarByClass(WordCountDriver.class);//        3.关联map和reducejob.setMapperClass(WordCountMapper.class);job.setReducerClass(WordCountReducer.class);//        4.设置map得输出kv类型job.setMapOutputKeyClass(Text.class);job.setMapOutputValueClass(IntWritable.class);//        5.设置最终输出得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(args[0]));FileOutputFormat.setOutputPath(job,new Path(args[1]));//        7.提交jobboolean result=job.waitForCompletion(true);System.out.println(result?"任务提交成功":"任务提交失败");}}

4. pom.xml

重点是更改添加打包插件依赖

<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></configuration><executions><execution><id>make-assembly</id><phase>package</phase><goals><goal>single</goal></goals></execution></executions></plugin>
</plugins>

pom.xml文件内容如下：

<?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>org.example</groupId><artifactId>mapreduce_demo</artifactId><version>1.0-SNAPSHOT</version><name>mapreduce_demo</name><!-- FIXME change it to the project's website --><url>http://www.example.com</url><properties><project.build.sourceEncoding>UTF-8</project.build.sourceEncoding><maven.compiler.source>1.8</maven.compiler.source><maven.compiler.target>1.8</maven.compiler.target></properties><dependencies><!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-client --><dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-client</artifactId><version>3.1.3</version></dependency></dependencies><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></configuration><executions><execution><id>make-assembly</id><phase>package</phase><goals><goal>single</goal></goals></execution></executions></plugin></plugins></build></project>

二、相关操作和配置

1. 项目打包

2. 带参测试

在本地执行成功：

3. 上传打包后的jar包和测试文档

上传打包后的带依赖jar包（第二个）和测试文档Hello.txt 到linux系统及hdfs上

cd /opt/jar/
ll

jar包改名：

mv mapreduce_demo-1.0-SNAPSHOT-jar-with-dependencies.jar wordcount.jar
ll

 cd /opt/file/ll

4. 增大虚拟内存

进行MapReduce操作时，可能会报溢出虚拟内存的错误

beyond the 'VIRTUAL’memory limit.
Current usage: 32.7 MB of 1 GB physical memory used;
2.3 GB of 2.1 GB virtual memory used. Killing container.

解决:

在mapred-site.xml中添加如下内容

	<!-- 是否对容器强制执行虚拟内存限制 --><property><name>yarn.nodemanager.vmem-check-enabled</name><value>false</value><description>Whether virtual memory limits will be enforced for containers</description></property><!-- 为容器设置内存限制时虚拟内存与物理内存之间的比率 --><property><name>yarn.nodemanager.vmem-pmem-ratio</name><value>5</value><description>Ratio between virtual memory to physical memory when setting memory limits for containers</description></property>

cd /opt/softs/hadoop3.1.3/etc/hadoop/
vim mapred-site.xml

分发到另外两台服务器虚拟机

scp mapred-site.xml root@bigdata04:/opt/softs/hadoop3.1.3/etc/hadoop/scp mapred-site.xml root@bigdata05:/opt/softs/hadoop3.1.3/etc/hadoop/

5.启动集群

[root@bigdata03 hadoop]# start-dfs.sh

[root@bigdata05 ~]# start-yarn.sh

6.在hdfs上创建输入文件夹和上传测试文档Hello.txt

hadoop fs -ls /
hadoop fs -mkdir /inputhadoop fs -put Hello.txt  /input
hadoop fs -ls  /input

7. 利用jar包在hdfs实现文本计数

 cd /opt/jar/llhadoop jar wordcount.jar org.example.wordcounttemplate.WordCountDriver /input/Hello.txt /output  

注意：输出目录需不存在，让他执行命令时自行创建

8. 查看计算统计结果

hadoop fs -ls  /output
hadoop fs -cat  /output/part-r-00000

对照文章：
大数据作业4（含在本地实现wordcount案例）
https://blog.csdn.net/m0_48170265/article/details/130029532?spm=1001.2014.3001.5501