WordCount案例实操
java代码
WordCountMapper类
package com.guodaxia.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> {
//1. 定义 k - v (文本 - 数量)
Text k = new Text();
IntWritable v = new IntWritable(1);//必须初始为1
//2，重写map方法，业务代码
@Override
protected void map(LongWritable key,Text value ,Context context) throws IOException, InterruptedException {
//1.从数据源中获得一行数据
String line = value.toString();
//2.分割该行中的单词
String[] words = line.split(" ");
//3.输出
for(String word:words){
k.set(word);//不对v做任何操作
context.write(k,v);//TODO
}
}
}
WordCountReducer类
package com.guodaxia.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> {
int sumTemp;
IntWritable v = new IntWritable(1);//必须初始为1
//业务逻辑代码
@Override
protected void reduce(Text key ,Iterable<IntWritable> values,Context context) throws IOException, InterruptedException {
//1.合并同一个word的出现次数
sumTemp = 0;
for (IntWritable count:values){
sumTemp += count.get();
}
v.set(sumTemp);
//2，输出
context.write(key,v);
}
}
WordCountDriver类
package com.guodaxia.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, InterruptedException, ClassNotFoundException {
// 1.获得配置信息和job对象
Configuration con = new Configuration();
Job job = Job.getInstance(con);
// 2.关联Driver程序的jar
job.setJarByClass(WordCountDriver.class);
// 3.关联Mapper和Reducer程序的jar
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k-v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job
boolean result = job.waitForCompletion(true);
System.exit(result ? 0 : 1);
}
}
xml配置文件
<?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>guodaxia</groupId>
<artifactId>MapReduceDemo</artifactId>
<version>1.0-SNAPSHOT</version>
<properties>
<maven.compiler.source>8</maven.compiler.source>
<maven.compiler.target>8</maven.compiler.target>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
</properties>
<dependencies>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>3.1.3</version>
</dependency>
<dependency>
<groupId>junit</groupId>
<artifactId>junit</artifactId>
<version>4.12</version>
</dependency>
<dependency>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-log4j12</artifactId>
<version>1.7.30</version>
</dependency>
<dependency>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-assembly-plugin</artifactId>
<version>3.2.0</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>
resources --> log4j.properties
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
Maven 打包
- 打开右置Maven --> Lifecycle -->package 等待打包完成
- 复制项目下target的 MapReduceDemo-1.0-SNAPSHOT.jar 到桌面并命名为wc.jar
hadoop集群环节
利用xshell软件将该wc.jar复制到 Hadoop集群下的/opt/module/hadoop-3.1.3下；
启动集群；
在当前路径下创建一个input 文件，并上传到Hadoop集群上；hadoop fs -put ./input input;
复制javaDriver驱动类的全包路径（打开该wordCountDriver类，在代码里右键）；
执行程序（集群上不能有output路径！！！）
hadoop jar wc.jar 驱动包 /input /output