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7.编写mapreduce案例

在写一个mapreduce类之前先添加依赖包

<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.it19gong</groupId>
<artifactId>testmaven</artifactId>
<version>0.0.1-SNAPSHOT</version>
<packaging>jar</packaging>

<name>testmaven</name>
<url>http://maven.apache.org</url>

<properties>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
</properties>

<dependencies>
<dependency>
<groupId>junit</groupId>
<artifactId>junit</artifactId>
<version>3.8.1</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>jdk.tools</groupId>
<artifactId>jdk.tools</artifactId>
<version>1.8</version>
<scope>system</scope>
<systemPath>${JAVA_HOME}/lib/tools.jar</systemPath>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-common</artifactId>
<version>2.6.0</version>
</dependency>

<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-hdfs</artifactId>
<version>2.6.0</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>2.6.0</version>
</dependency>

<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>2.6.0</version>
</dependency>

<dependency>
<groupId>org.apache.mrunit</groupId>
<artifactId>mrunit</artifactId>
<version>1.1.0</version>
<classifier>hadoop2</classifier>
<scope>test</scope>
</dependency>

<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-mapreduce-client-core</artifactId>
<version>2.6.0</version>
</dependency>

<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-yarn-api</artifactId>
<version>2.6.0</version>
</dependency>

<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-auth</artifactId>
<version>2.6.0</version>
</dependency>


<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-minicluster</artifactId>
<version>2.6.0</version>
<scope>test</scope>
</dependency>

<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-mapreduce-client-jobclient</artifactId>
<version>2.6.0</version>
<scope>provided</scope>
</dependency>

</dependencies>
</project>

 

 

新建一个WordCountMapper类

7.编写mapreduce案例_hadoop

 

 

 7.编写mapreduce案例_hadoop_02

 

package com.it19gong.testmaven;

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;

public class WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable>{
@Override
protected void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException
{
//拿到一行数据转换为string
String line = value.toString();
//将这一行切分出各个单词
String[] words = line.split(" ");
//遍历数组,输出<单词,1>
for(String word:words)
{
context.write(new Text(word), new IntWritable(1));
}
}
}

 

 

 

 

 定义WordCountReducer类

7.编写mapreduce案例_apache_03

package com.it19gong.testmaven;

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

public class WordCountReducer extends Reducer<Text,IntWritable,Text,IntWritable>{
@Override
protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
//定义一个计数器
int count = 0;
//遍历这一组kv的所有v,累加到count中
for(IntWritable value:values){
count += value.get();
}
context.write(key, new IntWritable(count));
}
}

 

 

定义WordCountRunner类

7.编写mapreduce案例_hadoop_04

 

package com.it19gong.testmaven;

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;


public class WordCountRunner {
//把业务逻辑相关的信息(哪个是mapper,哪个是reducer,要处理的数据在哪里,输出的结果放哪里……)描述成一个job对象
//把这个描述好的job提交给集群去运行
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
Job wcjob = Job.getInstance(conf);
//指定我这个job所在的jar包
// wcjob.setJar("/home/hadoop/wordcount.jar");
wcjob.setJarByClass(WordCountRunner.class);

wcjob.setMapperClass(WordCountMapper.class);
wcjob.setReducerClass(WordCountReducer.class);
//设置我们的业务逻辑Mapper类的输出key和value的数据类型
wcjob.setMapOutputKeyClass(Text.class);
wcjob.setMapOutputValueClass(IntWritable.class);
//设置我们的业务逻辑Reducer类的输出key和value的数据类型
wcjob.setOutputKeyClass(Text.class);
wcjob.setOutputValueClass(IntWritable.class);

//指定要处理的数据所在的位置
// FileInputFormat.setInputPaths(wcjob, "hdfs://hdp-server01:9000/wordcount/data/big.txt");
FileInputFormat.setInputPaths(wcjob, new Path(args[0]));
//指定处理完成之后的结果所保存的位置
// FileOutputFormat.setOutputPath(wcjob, new Path("hdfs://hdp-server01:9000/wordcount/output/"));
FileOutputFormat.setOutputPath(wcjob, new Path(args[1]));

//向yarn集群提交这个job
boolean res = wcjob.waitForCompletion(true);
System.exit(res?0:1);
}
}

 

 

打成架包

7.编写mapreduce案例_hadoop_05

7.编写mapreduce案例_maven_06

 

 7.编写mapreduce案例_maven_07

 

 把打包好的架包上传到集群

7.编写mapreduce案例_maven_08

 

 然后在集群上运行一个wordcount小案例

hadoop jar mr.jar  com.it19gong.testmaven.WordCountRunner /wc_input /wc_output

7.编写mapreduce案例_apache_09

7.编写mapreduce案例_apache_10

7.编写mapreduce案例_hadoop_11

 

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