Hadoop示例程序之——WordCount

尝试一下Hadoop的Demo，熟悉下相关的MapReducer的计算框架

1、创建Mapper类

package hadoop.mr;

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;
import java.util.StringTokenizer;

public class WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable> {

private Text word = new Text();

@Override
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
String wordline = value.toString();

StringTokenizer st = new StringTokenizer(wordline);
while (st.hasMoreTokens()) {
word.set(st.nextToken());
context.write(word, new IntWritable(1));
}
}
}

2、创建Reducer类

package hadoop.mr;

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> {

@Override
protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
int count = 0;
for (IntWritable value : values) {
count += value.get();
}
context.write(key, new IntWritable(count));
}
}

3、配置相关的Job作业

package hadoop.job;

import hadoop.mr.WordCountMapper;
import hadoop.mr.WordCountReducer;
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.mapred.FileInputFormat;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;

import java.io.IOException;

public class WordCount {
public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException {
Configuration config = new Configuration();
Job job = new Job(config);
job.setJarByClass(WordCount.class);
job.setJobName("wordCount-Job");
job.setMapperClass(WordCountMapper.class);
job.setReducerClass(WordCountReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
job.setInputFormatClass(TextInputFormat.class);
job.setOutputFormatClass(TextOutputFormat.class);
FileInputFormat.addInputPath((JobConf) job.getConfiguration(), new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
job.waitForCompletion(true);
}
}

4、引入相关依赖

由于我这边使用的是ide编辑器来进行编码的，并且是创建的相关的maven工程进行项目的管理，因此需要引入一下的依赖

<dependencies>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-common</artifactId>
<version>2.5.2</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-hdfs</artifactId>
<version>2.5.2</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>2.5.2</version>
</dependency>
</dependencies>

5、打成jar包

<groupId>FirstTry</groupId>
<artifactId>FirstTry</artifactId>
<version>1.0-SNAPSHOT</version>

6、由于使用hdfs进行文件的上传

相关命令如下：

hadoop fs -mkdir hdfs://localhost:9000/user/input --创建Hadoop存放文件的路径

hadoop fs -put FileTest1.txt hdfs://localhost:9000/user/input --上传文件至Hdfs 具体的相关原理后期再讲

hadoop fs -ls hdfs://localhost:9000/user/inout --查看hdfs中相关上传的文件

7、运行jar包，执行相关的作业

相关命令如下：

1、进入到相关的jar包所在路径下 此处我的jar包应该是打成FirstTry.jar,因此进入到FirstTry.jar的路径下 执行以下相关命令

hadoop jar FirstTry.jar hdfs://localhost:9000/user/input hdfs://localhost:9000/user/output

如果出现提示：Exception in thread "main" java.io.IOException: Mkdirs failed to create ..... 需要将jar包中的META-INF/LICENSE文 件夹删除，找到相关的jar -shift+delete 删除相关的目录

2、由于我的jar包是FirstTry.jar 而我的main函数的class是WordCount，因此需要制定下相关的class，因此相关的Hadoop命令改成这样，

hadoop jar FirstTry.jar hadoop.job.WordCount hdfs://locahost:9000/user/input hdfs://localhost:9000/user/output

然后你会看见下面的流程：



说明我们的第一个demo执行成功。

8、查询相关的结果

hadoop fs -ls hdfs://localhost:9000/user/output 查看输出文件part-r-00000

查看文件内容 hadoop fs -cat hdfs://localhost:9000/user/output/part-r-00000

9、计算单词数量成功

第一个小程序OK啦。