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