文章目录
1. 准备工作
- pom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>cn.itcast</groupId>
<artifactId>mapreduce</artifactId>
<version>1.0-SNAPSHOT</version>
<repositories>
<repository>
<id>cloudera</id>
<url>https://repository.cloudera.com/artifactory/cloudera-repos/</url>
</repository>
</repositories>
<dependencies>
<dependency>
<groupId>org.apache.Hadoop</groupId>
<artifactId>Hadoop-client</artifactId>
<version>2.6.0-mr1-cdh5.14.0</version>
</dependency>
<dependency>
<groupId>org.apache.Hadoop</groupId>
<artifactId>Hadoop-common</artifactId>
<version>2.6.0-cdh5.14.0</version>
</dependency>
<dependency>
<groupId>org.apache.Hadoop</groupId>
<artifactId>Hadoop-hdfs</artifactId>
<version>2.6.0-cdh5.14.0</version>
</dependency>
<dependency>
<groupId>org.apache.Hadoop</groupId>
<artifactId>Hadoop-mapreduce-client-core</artifactId>
<version>2.6.0-cdh5.14.0</version>
</dependency>
<dependency>
<groupId>junit</groupId>
<artifactId>junit</artifactId>
<version>4.11</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>org.testng</groupId>
<artifactId>testng</artifactId>
<version>RELEASE</version>
</dependency>
</dependencies>
<build>
<plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.0</version>
<configuration>
<source>1.8</source>
<target>1.8</target>
<encoding>UTF-8</encoding>
</configuration>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-shade-plugin</artifactId>
<version>2.4.3</version>
<executions>
<execution>
<phase>package</phase>
<goals>
<goal>shade</goal>
</goals>
<configuration>
<minimizeJar>true</minimizeJar>
</configuration>
</execution>
</executions>
</plugin>
</plugins>
</build>
</project>
2. WordCount计算
计算每个单词出现的次数
2.1 原始数据
zhangsan,lisi,wangwu
zhaoliu,maqi
zhangsan,zhaoliu,wangwu
lisi,wangwu
2.2 期望的结果
zhangsan 2
lisi 2
wangwu 3
zhaoliu 2
maqi 1
2.3 偏移量
每个字符移动到当前文档的最前面需要移动的字符个数。
2.4 WordCount-Map实现
注意：导包别导错了
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, LongWritable> {
@Override
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
//获取传入的每一行内容
String line = value.toString();
//按照数据分隔符切割
String[] words = line.split(" ");
//遍历单词数组,出现单词就标记为1
for (String word : words) {
context.write(new Text(word), new LongWritable(1));
}
}
}
1、实例一个class 继承Mapper<输入的key的数据类型，输入的value的数据类型，输出的key的数据类型，输出的value的数据类型>
2、重写map方法 map(LongWritable key, Text value, Context context)
key: 行首字母的偏移量
value: 一行数据
context：上下文对象
3、根据业务需求进行切分，然后逐一输出
2.5 WordCount-Reduce实现
注意：导包别导错了
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
import java.io.IOException;
public class WordCountReducer extends Reducer<Text,LongWritable,Text,LongWritable> {
@Override
protected void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException {
//声明一个变量
int sum = 0;
for (LongWritable value: values) {
sum += value.get();
}
context.write(key, new LongWritable(sum));
}
}
1、实例一个class 继承Reducer<输入的key的数据类型，输入的value的数据类型，输出的key的数据类型，输出的value的数据类型>
2、重写reduce方法 reduce(Text key, Iterable values, Context context)
key: 去重后单词
values： 标记的1（好多个1，key出现几次就有几个1）
context：上下文对象
3、遍历values 进行汇总计算
2.6 WordCount-Driver实现
注意：导包别导错了
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
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 {
public static void main(String[] args) throws Exception {
// 创建本次mr程序的job实例
Configuration conf = new Configuration();
// conf.set("mapreduce.framework.name", "local");
Job job = Job.getInstance(conf);
// 指定本次job运行的主类
job.setJarByClass(WordCountRunner.class);
// 指定本次job的具体mapper reducer实现类
job.setMapperClass(WordCountMapper.class);
job.setReducerClass(WordCountReducer.class);
// 指定本次job map阶段的输出数据类型
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(LongWritable.class);
// 指定本次job reduce阶段的输出数据类型 也就是整个mr任务的最终输出类型
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(LongWritable.class);
// 指定本次job待处理数据的目录 和程序执行完输出结果存放的目录
long startTime=System.currentTimeMillis(); //获取开始时间
FileInputFormat.setInputPaths(job,"E:\\wordcount\\input\\words.txt");
FileOutputFormat.setOutputPath(job, new Path("E:\\wordcount\\output"));
// 提交本次job
boolean b = job.waitForCompletion(true);
long endTime=System.currentTimeMillis(); //获取结束时间
System.out.println("程序运行时间： "+(endTime-startTime)+"ms");
System.exit(b ? 0 : 1);
}
}
2.7 最终结果
lisi 2
maqi 1
wangwu 3
zhangsan 2
zhaoliu 2
