使用Maven搭建Hadoop开发环境

关于Maven的使用就不再啰嗦了，网上很多，并且这么多年变化也不大，这里仅介绍怎么搭建Hadoop的开发环境。

1. 首先创建工程

mvn archetype:generate -DgroupId=my.hadoopstudy -DartifactId=hadoopstudy -DarchetypeArtifactId=maven-archetype-quickstart -DinteractiveMode=false

2. 然后在pom.xml文件里添加hadoop的依赖包hadoop-common, hadoop-client, hadoop-hdfs，添加后的pom.xml文件如下

<project xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://maven.apache.org/POM/4.0.0"
 xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>my.hadoopstudy</groupId>
<artifactId>hadoopstudy</artifactId>
<packaging>jar</packaging>
<version>1.0-SNAPSHOT</version>
<name>hadoopstudy</name>
<url>http://maven.apache.org</url>

<dependencies>
<dependency>
 <groupId>org.apache.hadoop</groupId>
 <artifactId>hadoop-common</artifactId>
 <version>2.5.1</version>
</dependency>
<dependency>
 <groupId>org.apache.hadoop</groupId>
 <artifactId>hadoop-hdfs</artifactId>
 <version>2.5.1</version>
</dependency>
<dependency>
 <groupId>org.apache.hadoop</groupId>
 <artifactId>hadoop-client</artifactId>
 <version>2.5.1</version>
</dependency>

<dependency>
 <groupId>junit</groupId>
 <artifactId>junit</artifactId>
 <version>3.8.1</version>
 <scope>test</scope>
</dependency>
</dependencies>
</project>

3. 测试

3.1 首先我们可以测试一下hdfs的开发，这里假定使用上一篇Hadoop文章中的hadoop集群，类代码如下

package my.hadoopstudy.dfs;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FSDataOutputStream;
import org.apache.hadoop.fs.FileStatus;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IOUtils;

import java.io.InputStream;
import java.net.URI;

public class Test {
public static void main(String[] args) throws Exception {
String uri = "hdfs://9.111.254.189:9000/";
Configuration config = new Configuration();
FileSystem fs = FileSystem.get(URI.create(uri), config);

// 列出hdfs上/user/fkong/目录下的所有文件和目录
FileStatus[] statuses = fs.listStatus(new Path("/user/fkong"));
for (FileStatus status : statuses) {
 System.out.println(status);
}

// 在hdfs的/user/fkong目录下创建一个文件，并写入一行文本
FSDataOutputStream os = fs.create(new Path("/user/fkong/test.log"));
os.write("Hello World!".getBytes());
os.flush();
os.close();

// 显示在hdfs的/user/fkong下指定文件的内容
InputStream is = fs.open(new Path("/user/fkong/test.log"));
IOUtils.copyBytes(is, System.out, 1024, true);
}
}

3.2 测试MapReduce作业

测试代码比较简单，如下：

package my.hadoopstudy.mapreduce;

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.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;

import java.io.IOException;

public class EventCount {

public static class MyMapper extends Mapper<Object, Text, Text, IntWritable>{
private final static IntWritable one = new IntWritable(1);
private Text event = new Text();

public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
 int idx = value.toString().indexOf(" ");
 if (idx > 0) {
 String e = value.toString().substring(0, idx);
 event.set(e);
 context.write(event, one);
 }
}
}

public static class MyReducer extends Reducer<Text,IntWritable,Text,IntWritable> {
private IntWritable result = new IntWritable();

public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
 int sum = 0;
 for (IntWritable val : values) {
 sum += val.get();
 }
 result.set(sum);
 context.write(key, result);
}
}

public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
if (otherArgs.length < 2) {
 System.err.println("Usage: EventCount <in> <out>");
 System.exit(2);
}
Job job = Job.getInstance(conf, "event count");
job.setJarByClass(EventCount.class);
job.setMapperClass(MyMapper.class);
job.setCombinerClass(MyReducer.class);
job.setReducerClass(MyReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}

运行“mvn package”命令产生jar包hadoopstudy-1.0-SNAPSHOT.jar，并将jar文件复制到hadoop安装目录下

这里假定我们需要分析几个日志文件中的Event信息来统计各种Event个数，所以创建一下目录和文件

/tmp/input/event.log.1
/tmp/input/event.log.2
/tmp/input/event.log.3

因为这里只是要做一个列子，所以每个文件内容可以都一样，假如内容如下

JOB_NEW ...
JOB_NEW ...
JOB_FINISH ...
JOB_NEW ...
JOB_FINISH ...

然后把这些文件复制到HDFS上

$ bin/hdfs dfs -put /tmp/input /user/fkong/input

运行mapreduce作业

$ bin/hadoop jar hadoopstudy-1.0-SNAPSHOT.jar my.hadoopstudy.mapreduce.EventCount /user/fkong/input /user/fkong/output

查看执行结果

$ bin/hdfs dfs -cat /user/fkong/output/part-r-00000

来源：http://www.kongxx.info/blog/?p=186