使用Maven搭建Hadoop开发环境

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

复制代码 代码如下:

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

xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">

4.0.0

my.hadoopstudy

hadoopstudy

jar

1.0-SNAPSHOT

hadoopstudy

http://maven.apache.org

org.apache.hadoop

hadoop-common

2.5.1

org.apache.hadoop

hadoop-hdfs

2.5.1

org.apache.hadoop

hadoop-client

2.5.1

junit

junit

3.8.1

test

xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">

4.0.0

my.hadoopstudy

hadoopstudy

jar

1.0-SNAPSHOT

hadoopstudy

http://maven.apache.org

org.apache.hadoop

hadoop-common

2.5.1

org.apache.hadoop

hadoop-hdfs

2.5.1

org.apache.hadoop

hadoop-client

2.5.1

junit

junit

3.8.1

test

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

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 {

private IntWritable result = new IntWritable();

public void reduce(Text key, Iterable 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 ");

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qrBxzkK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

复制代码 代码如下:$ bin/hdfs dfs -put /tmp/input /user/fkong/input

运行mapreduce作业

复制代码 代码如下:

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

查看执行结果

复制代码 代码如下:$ bin/hdfs dfs -cat /user/fkong/output/part-r-00000

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