使用Maven搭建Hadoop开发环境

worker321

　　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=&quot;http://www.w3.org/2001/XMLSchema-instance&quot; xmlns=&quot;http://maven.apache.org/POM/4.0.0&quot;
xsi:schemaLocation=&quot;http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd&quot;>
<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 = &quot;hdfs://9.111.254.189:9000/&quot;;
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(&quot;/user/fkong&quot;));
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(&quot;/user/fkong/test.log&quot;));
os.write(&quot;Hello World!&quot;.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(&quot;/user/fkong/test.log&quot;));
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(&quot; &quot;);
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(&quot;Usage: EventCount <in> <out>&quot;);
System.exit(2);
}
Job job = Job.getInstance(conf, &quot;event count&quot;);
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

　　

   
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