一、需求分析
- 需求：在给定的文本文件中统计输出每一个单词出现的总次数
- SEVENTEEN.txt文本内容如下：
say the name seventeen
hello
we are seventeen
nice to meet you
you
very nice
- 按照MapReduce编程规范，分别编写Mapper，Reducer，Driver
1、Mapper
（1）将MapTask传过来的文本内容先转换成String
（2）根据空格将这一行切分成单词
（3）将单词输出为<单词，1>
2、Reducer
（1）汇总各个key的个数
（2）输出该key的总次数
3、Driver
（1）获取配置信息，获取job对象实例
（2）指定本程序的jar包所在的本地路径
（3）关联Mapper/Reducer业务类
（4）指定Mapper输出数据的kv类型
（5）指定最终输出的数据的kv类型
（6）指定job的输入原始文件所在目录
（7）指定job的输出结果所在目录
（8）提交作业
二、环境准备
1、创建maven工程，MapReduceDemo
2、在pom.xml文件中添加如下依赖
<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>
</dependencies>
3、在项目的src/main/resources目录下，新建一个文件，命名为“log4j.properties”，在文件中填入
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
4、创建包名：com.user.mapreduce.wordcount
三、编写程序
1、编写Mapper类
package com.user.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> {Text k = new Text();IntWritable v = new IntWritable(1);@Overrideprotected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
// 1 获取一行String line = value.toString();
// 2 切割String[] words = line.split(" ");
// 3 输出for (String word : words) {k.set(word);context.write(k, v);}}
}2、编写Reducer类
package com.user.mapreduce.wordcount;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>{int sum;IntWritable v = new IntWritable();@Overrideprotected void reduce(Text key, Iterable<IntWritable> values,Context context) throws IOException, InterruptedException {
// 1 累加求和sum = 0;for (IntWritable count : values) {sum += count.get();}
// 2 输出v.set(sum);context.write(key,v);}
}
3、编写Driver驱动类
package com.user.mapreduce.wordcount;import java.io.IOException;
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 WordCountDriver {public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
// 1 获取配置信息以及获取 job 对象Configuration conf = new Configuration();Job job = Job.getInstance(conf);
// 2 关联本 Driver 程序的 jarjob.setJarByClass(WordCountDriver.class);
// 3 关联 Mapper 和 Reducer 的 jarjob.setMapperClass(WordCountMapper.class);job.setReducerClass(WordCountReducer.class);
// 4 设置 Mapper 输出的 kv 类型job.setMapOutputKeyClass(Text.class);job.setMapOutputValueClass(IntWritable.class);
// 5 设置最终输出 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("C:\\Users\\shi.hongpin\\Desktop\\SEVENTEEN.txt"));FileOutputFormat.setOutputPath(job, new Path("D:\\hadoop\\output"));
// 7 提交 jobboolean result = job.waitForCompletion(true);System.exit(result ? 0 : 1);}
}
- 打包成jar包，到虚拟机运行，输入输出路径要修改为：
FileInputFormat.setInputPaths(job, new Path(arg[0]));
FileOutputFormat.setOutputPath(job, new Path(arg[1]));
5、本地运行成功后在对应的输出路径能看到输出结果
are 1
hello 1
meet 1
name 1
nice 2
say 1
seventeen 2
the 1
to 1
very 1
we 1
you 2
四、提交到集群测试
1、用maven打jar包，需要添加的打包插件依赖
<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><executions><execution><id>make-assembly</id><phase>package</phase><goals><goal>single</goal></goals></execution></executions></configuration></plugin></plugins></build>
2、将程序打包成jar包，修改名称为wc.jar，并将其拷贝到Hadoop集群/opt/module/hadoop-3.1.3 路径
3、执行WordCount程序
hadoop-3.1.3]$ hadoop jar wc.jar com.user.mapreduce.wordcount.WordCountDriver /wcinput/ /wcoutput7
- 使用JavaApi实现离线文本上传
