Maven搭建Hadoop应用开发环境

目录

开发环境

Hadoop版本:2.7.2
Eclipse版本:Luna 4.4.1

创建maven工程

如果安装了maven可以通过maven命令行创建简单工程,命令为:

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

也可以通过eclipse直接创建maven项目

pom依赖

在pom.xml文件添加Hadoop依赖,内容如下:

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<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>

这样一个基础的Hadoop工程就构建起来了!

实战

为了测试做一些简单的开发.

HDFS开发

首先测试一下HDFS开发,以之前的伪分布式环境为前提:http://kiritor.github.io/2016/04/24/Hadoop-install/
1.启动Hadoop,命令为:

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bin/start-all.sh

2.编码
这里通过程序创建文件,列出文件,输出指定文件内容,代码如下:

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package com.lcore.hadoop;
import java.io.InputStream;
import java.net.URI;
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;
public class Test {
public static void main(String[] args) throws Exception {
String uri = "hdfs://localhost: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("/test/"));
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("/test/hadoop.log"));
os.write("开启我的Hadoop之旅".getBytes());
os.flush();
os.close();
// 显示在hdfs的指定文件的内容
InputStream is = fs.open(new Path("/test/hadoop.log"));
IOUtils.copyBytes(is, System.out, 1024, true);
}
}

3.运行
直接application或者run on hadoop都可以,必须确保hadoop启动.输出结果如下:

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FileStatus{path=hdfs://localhost:9000/test/hadoop.log; isDirectory=false; length=24; replication=3; blocksize=134217728; modification_time=1461652343934; access_time=1461652342969; owner=lcore; group=supergroup; permission=rw-r--r--; isSymlink=false}
FileStatus{path=hdfs://localhost:9000/test/hexo.md; isDirectory=false; length=1443; replication=1; blocksize=134217728; modification_time=1461565620758; access_time=1461569568851; owner=lcore; group=supergroup; permission=rw-r--r--; isSymlink=false}
FileStatus{path=hdfs://localhost:9000/test/input; isDirectory=true; modification_time=1461652730181; access_time=0; owner=lcore; group=supergroup; permission=rwxr-xr-x; isSymlink=false}
FileStatus{path=hdfs://localhost:9000/test/out; isDirectory=true; modification_time=1461565736828; access_time=0; owner=lcore; group=supergroup; permission=rwxr-xr-x; isSymlink=false}
开启我的Hadoop之旅

MapReduce开发

功能描述:统计出现的名字次数.
1.编码

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package com.lcore.hadoop;
import java.io.IOException;
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;
public class NameCount {
public static class MyMapper extends Mapper<Object, Text, Text, IntWritable>{
private final static IntWritable one = new IntWritable(1);
private Text name= 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);
name.set(e);
context.write(name, 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: NameCount <in> <out>");
System.exit(2);
}
Job job = Job.getInstance(conf, "name count");
job.setJarByClass(NameCount.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);
}
}

2.通过maven将打成的jar放在hadoop安装目录:share/hadoop/mapreduce/下.
3.创建一下目录及文件用于分析

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event.log.1
event.log.2
event.log.3

文件内容格式为:

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Kiritor ...
LCore ...

之后复制文件到HDFS上

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bin/hdfs dfs -mkdir /test/input #新建目录
bin/hdfs dfs -put ~/Documents/event.log.2 /test/input #复制文件
bin/hdfs dfs -put ~/Documents/event.log.2 /test/input
bin/hdfs dfs -put ~/Documents/event.log.2 /test/input

4.运行
使用如下命令运行:

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bin/hadoop jar share/hadoop/mapreduce/hadoop-0.0.1-SNAPSHOT.jar com.lcore.hadoop.EventCount /test/input /test/input/out

执行结果存放在/test/input/out目录下
5.查看执行结果
使用如下命令查看执行结果:

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bin/hdfs dfs -cat /test/input/out/part-r-00000
#执行结果
KK 2
Kiritor 3
L.Tao 4
LCore 5

over!


本文链接: http://kiritor.github.io/2016/04/26/Hadoop-dev-install/

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