使用maven搭建hadoop环境

最新推荐文章于 2022-05-16 19:37:00 发布

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最新推荐文章于 2022-05-16 19:37:00 发布

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分类专栏： hadoop 文章标签： maven依赖

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本文介绍了如何使用Maven搭建Hadoop的开发环境，包括在pom.xml中添加hadoop的相关依赖，以及测试HDFS和MapReduce作业的步骤。通过运行`mvn package`生成jar包并提交到Hadoop集群进行执行。

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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

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

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
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<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>2.6.4</version>  
        </dependency>  
        <dependency>  
            <groupId>org.apache.hadoop</groupId>  
            <artifactId>hadoop-hdfs</artifactId>  
            <version>2.6.4</version>  
        </dependency>  
        <dependency>  
            <groupId>org.apache.hadoop</groupId>  
            <artifactId>hadoop-client</artifactId>  
            <version>2.6.4</version>  
        </dependency>  

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

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

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

然后把这些文件复制到HDFS上
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$ bin/hdfs dfs -put /tmp/input /user/fkong/input

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

查看执行结果
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$ bin/hdfs dfs -cat /user/fkong/output/part-r-00000