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
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
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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>2.5.1</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
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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.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
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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
此命令包含一系列过程:validate-->compile-->test-->package;
Maven根据pom文件里packaging的配置,决定是生成jar文件还是war文件,并放到target目录下。
(注:此命令需要在项目的根目录(也就是pom.xml文件所在的目录)下运行,Maven才知道打包哪个项目。)
如何用maven打包可执行的jar
方法一:将项目及所依赖的所有jar包打包成一个jar。
1、pom.xml添加assembly插件
2、执行mvn assembly:assembly
3、生成如文件
4、执行java -jar quickstart-1.0.0-jar-with-dependencies.jar 备注:第2步的命令也可以改成mvn package,但需要更改pom.xml:
方法二:将项目依赖的jar复制到一个目录中并在MANIFEST文件中添加Class-Path和Main-Class。
1、在pom.xml中添加jar和dependency插件
2、执行mvn package
3、生成文件
4、执行java -jar quickstart-1.0.0.jar
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