使用Maven搭建Hadoop2开发环境

关于Maven的使用会在另外分享中说明，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，这里假定使用本博上一篇Hadoop文章中的hadoop伪分布式，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://192.168.1.140:9000/";
        Configuration config = new Configuration();
        FileSystem fs = FileSystem.get(URI.create(uri), config);
 
        // 列出hdfs上/根目录下的所有文件和目录
        FileStatus[] statuses = fs.listStatus(new Path("/"));
        for (FileStatus status : statuses) {
            System.out.println(status);
        }
 
        // 在hdfs的/user根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/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/input /user/output  

 

查看执行结果

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