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原文地址: http://my.oschina.net/lanzp/blog/309078
目录[-]
1、开发配置环境:
开发环境:Win7(64bit)+Eclipse(kepler service release 2)
配置环境:Ubuntu Server 14.04.1 LTS(64-bit only)
辅助工具:WinSCP + Putty
Hadoop版本:2.5.0
Hadoop的Eclipse开发插件(2.x版本适用):http://pan.baidu.com/s/1eQy49sm
服务器端JDK版本:OpenJDK7.0
以上所有工具请自行下载安装。
2、Hadoop服务端配置(Master节点)
最近一直在摸索Hadoop2的配置,因为Hadoop2对原有的一些框架API做了调整,但也还是兼容旧版本的(包括配置)。像我这种就喜欢用新的东西的人,当然要尝一下鲜了,现在网上比较少新版本的配置教程,那么下面我就来分享一下我自己的实战经验,如有不正确的地欢迎指正:)。
假设我们已经成功地安装了Ubuntu Server、OpenJDK、SSH,如果还没有安装的话请先安装,自己网上找一下教程,这里我就说一下SSH的无口令登陆设置。首先通过
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$ ssh localhost
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测试一下自己有没有设置好无口令登陆,如果没有设置好,系统将要求你输入密码,通过下面的设置可以实现无口令登陆,具体原理请百度谷歌:
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$ ssh-keygen -t dsa -P '' -f ~/.ssh/id_dsa
$ cat ~/.ssh/id_dsa.pub >> ~/.ssh/authorized_keys |
其次是Hadoop安装(假设已经安装好OpenJDK以及配置好了环境变量),到Hadoop官网下载一个Hadoop2.5.0版本的下来,好像大概有100多M的tar.gz包,下载 下来后自行解压,我的是放在/usr/mywind下面,Hadoop主目录完整路径是/usr/mywind/hadoop,这个路径根据你个人喜好放吧。
解压完后,打开hadoop主目录下的etc/hadoop/hadoop-env.sh文件,在最后面加入下面内容:
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# set to the root of your Java installation export JAVA_HOME=/usr/lib/jvm/java- 7 -openjdk-amd64
# Assuming your installation directory is /usr/mywind/hadoop export HADOOP_PREFIX=/usr/mywind/hadoop |
为了方便起见,我建设把Hadoop的bin目录及sbin目录也加入到环境变量中,我是直接修改了Ubuntu的/etc/environment文件,内容如下:
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PATH= "/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/usr/lib/jvm/java-7-openjdk-amd64/bin:/usr/mywind/hadoop/bin:/usr/mywind/hadoop/sbin"
JAVA_HOME= "/usr/lib/jvm/java-7-openjdk-amd64"
CLASSPATH= ".:$JAVA_HOME/jre/lib/rt.jar:$JAVA_HOME/lib/dt.jar:$JAVA_HOME/lib/tools.jar"
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也可以通过修改profile来完成这个设置,看个人习惯咯。假如上面的设置你都完成了,可以在命令行里面测试一下Hadoop命令,如下图:
假如你能看到上面的结果,恭喜你,Hadoop安装完成了。接下来我们可以进行伪分布配置(Hadoop可以在伪分布模式下运行单结点)。
接下来我们要配置的文件有四个,分别是/usr/mywind/hadoop/etc/hadoop目录下的yarn-site.xml、mapred-site.xml、hdfs-site.xml、core-site.xml(注意:这个版本下默认没有yarn-site.xml文件,但有个yarn-site.xml.properties文件,把后缀修改成前者即可),关于yarn新特性可以参考官网或者这个文章http://www.ibm.com/developerworks/cn/opensource/os-cn-hadoop-yarn/。
首先是core-site.xml配置HDFS地址及临时目录(默认的临时目录在重启后会删除):
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< configuration >
< property >
< name >fs.defaultFS</ name >
< value >hdfs://192.168.8.184:9000</ value >
< description >same as fs.default.name</ description >
</ property >
< property >
< name >hadoop.tmp.dir</ name >
< value >/usr/mywind/tmp</ value >
< description >A base for other temporary directories.</ description >
</ property >
</ configuration >
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然后是hdfs-site.xml配置集群数量及其他一些可选配置比如NameNode目录、DataNode目录等等:
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< configuration >
< property >
< name >dfs.namenode.name.dir</ name >
< value >/usr/mywind/name</ value >
< description >same as dfs.name.dir</ description >
</ property >
< property >
< name >dfs.datanode.data.dir</ name >
< value >/usr/mywind/data</ value >
< description >same as dfs.data.dir</ description >
</ property >
< property >
< name >dfs.replication</ name >
< value >1</ value >
< description >same as old frame,recommend set the value as the cluster DataNode host numbers!</ description >
</ property >
</ configuration >
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接着是mapred-site.xml配置启用yarn框架:
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< configuration >
< property >
< name >mapreduce.framework.name</ name >
< value >yarn</ value >
</ property >
</ configuration >
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最后是yarn-site.xml配置NodeManager:
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< configuration >
<!-- Site specific YARN configuration properties --> < property >
< name >yarn.nodemanager.aux-services</ name >
< value >mapreduce_shuffle</ value >
</ property >
</ configuration >
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注意,网上的旧版本教程可能会把value写成mapreduce.shuffle,这个要特别注意一下的,至此我们所有的文件配置都已经完成了,下面进行HDFS文件系统进行格式化:
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$ hdfs namenode -format |
然后启用NameNode及DataNode进程:
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$ start-yarn.sh |
然后创建hdfs文件目录
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$ hdfs dfs -mkdir /user $ hdfs dfs -mkdir /user/a01513 |
注意,这个a01513是我在Ubuntu上的用户名,最好保持与系统用户名一致,据说不一致会有许多权限等问题,我之前试过改成其他名字,报错,实在麻烦就改成跟系统用户名一致吧。
然后把要测试的输入文件放在文件系统中:
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$ hdfs dfs -put /usr/mywind/psa input |
文件内容是Hadoop经典的天气例子的数据:
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12345679867623119010123456798676231190101234567986762311901012345679867623119010123456 + 001212345678903456
12345679867623119010123456798676231190101234567986762311901012345679867623119010123456 + 011212345678903456
12345679867623119010123456798676231190101234567986762311901012345679867623119010123456 + 021212345678903456
12345679867623119010123456798676231190101234567986762311901012345679867623119010123456 + 003212345678903456
12345679867623119010123456798676231190201234567986762311901012345679867623119010123456 + 004212345678903456
12345679867623119010123456798676231190201234567986762311901012345679867623119010123456 + 010212345678903456
12345679867623119010123456798676231190201234567986762311901012345679867623119010123456 + 011212345678903456
12345679867623119010123456798676231190501234567986762311901012345679867623119010123456 + 041212345678903456
12345679867623119010123456798676231190501234567986762311901012345679867623119010123456 + 008212345678903456
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把文件拷贝到HDFS目录之后,我们可以通过浏览器查看相关的文件及一些状态:
http://192.168.8.184:50070/
这里的IP地址根据你实际的Hadoop服务器地址啦。
好吧,我们所有的Hadoop后台服务搭建跟数据准备都已经完成了,那么我们的M/R程序也要开始动手写了,不过在写当然先配置开发环境了。
3、基于Eclipse的Hadoop2.x开发环境配置
关于JDK及ECLIPSE的安装我就不再介绍了,相信能玩Hadoop的人对这种配置都已经再熟悉不过了,如果实在不懂建议到谷歌百度去搜索一下教程。假设你已经把Hadoop的Eclipse插件下载下来了,然后解压把jar文件放到Eclipse的plugins文件夹里面:
重启Eclipse即可。
然后我们再安装Hadoop到Win7下,在这不再详细说明,跟安装JDK大同小异,在这个例子中我安装到了E:\hadoop。
启动Eclipse,点击菜单栏的【Windows/窗口】→【Preferences/首选项】→【Hadoop Map/Reduce】,把Hadoop Installation Directory设置成开发机上的Hadoop主目录:
点击OK。
开发环境配置完成,下面我们可以新建一个测试Hadoop项目,右键【NEW/新建】→【Others、其他】,选择Map/Reduce Project
输入项目名称点击【Finish/完成】:
创建完成后可以看到如下目录:
然后在SRC下建立下面包及类:
以下是代码内容:
TestMapper.java
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package com.my.hadoop.mapper;
import java.io.IOException;
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reporter;
public class TestMapper extends MapReduceBase implements Mapper<LongWritable, Text, Text, IntWritable> {
private static final int MISSING = 9999 ;
private static final Log LOG = LogFactory.getLog(TestMapper. class );
public void map(LongWritable key, Text value, OutputCollector<Text, IntWritable> output,Reporter reporter)
throws IOException {
String line = value.toString();
String year = line.substring( 15 , 19 );
int airTemperature;
if (line.charAt( 87 ) == '+' ) { // parseInt doesn't like leading plus signs
airTemperature = Integer.parseInt(line.substring( 88 , 92 ));
} else {
airTemperature = Integer.parseInt(line.substring( 87 , 92 ));
}
LOG.info( "loki:" +airTemperature);
String quality = line.substring( 92 , 93 );
LOG.info( "loki2:" +quality);
if (airTemperature != MISSING && quality.matches( "[012459]" )) {
LOG.info( "loki3:" +quality);
output.collect( new Text(year), new IntWritable(airTemperature));
}
}
} |
TestReducer.java
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package com.my.hadoop.reducer;
import java.io.IOException;
import java.util.Iterator;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.Reducer;
public class TestReducer extends MapReduceBase implements Reducer<Text, IntWritable, Text, IntWritable> {
@Override
public void reduce(Text key, Iterator<IntWritable> values, OutputCollector<Text, IntWritable> output,Reporter reporter)
throws IOException{
int maxValue = Integer.MIN_VALUE;
while (values.hasNext()) {
maxValue = Math.max(maxValue, values.next().get());
}
output.collect(key, new IntWritable(maxValue));
}
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TestHadoop.java
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package com.my.hadoop.test.main;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import com.my.hadoop.mapper.TestMapper;
import com.my.hadoop.reducer.TestReducer;
public class TestHadoop {
public static void main(String[] args) throws Exception{
if (args.length != 2 ) {
System.err
.println( "Usage: MaxTemperature <input path> <output path>" );
System.exit(- 1 );
}
JobConf job = new JobConf(TestHadoop. class );
job.setJobName( "Max temperature" );
FileInputFormat.addInputPath(job, new Path(args[ 0 ]));
FileOutputFormat.setOutputPath(job, new Path(args[ 1 ]));
job.setMapperClass(TestMapper. class );
job.setReducerClass(TestReducer. class );
job.setOutputKeyClass(Text. class );
job.setOutputValueClass(IntWritable. class );
JobClient.runJob(job);
}
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为了方便对于Hadoop的HDFS文件系统操作,我们可以在Eclipse下面的Map/Reduce Locations窗口与Hadoop建立连接,直接右键新建Hadoop连接即可:
连接配置如下:
然后点击完成即可,新建完成后,我们可以在左侧目录中看到HDFS的文件系统目录:
这里不仅可以显示目录结构,还可以对文件及目录进行删除、新增等操作,非常方便。
当上面的工作都做好之后,就可以把这个项目导出来了(导成jar文件放到Hadoop服务器上运行):
点击完成,然后把这个testt.jar文件上传到Hadoop服务器(192.168.8.184)上,目录(其实可以放到其他目录,你自己喜欢)是:
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/usr/mywind/hadoop/share/hadoop/mapreduce |
如下图:
4、运行Hadoop程序及查看运行日志
当上面的工作准备好了之后,我们运行自己写的Hadoop程序很简单:
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$ hadoop jar /usr/mywind/hadoop/share/hadoop/mapreduce/testt.jar com.my.hadoop.test.main.TestHadoop input output |
注意这是output文件夹名称不能重复哦,假如你执行了一次,在HDFS文件系统下面会自动生成一个output文件夹,第二次运行时,要么把output文件夹先删除($ hdfs dfs -rmr /user/a01513/output),要么把命令中的output改成其他名称如output1、output2等等。
如果看到以下输出结果,证明你的运行成功了:
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a01513@hadoop :~$ hadoop jar /usr/mywind/hadoop/share/hadoop/mapreduce/testt .jar com.my.hadoop. test .main.TestHadoop input output
14 /09/02 11:14:03 INFO client.RMProxy: Connecting to ResourceManager at /0 .0.0.0 :8032
14 /09/02 11:14:04 INFO client.RMProxy: Connecting to ResourceManager at /0 .0.0.0 :8032
14 /09/02 11:14:04 WARN mapreduce.JobSubmitter: Hadoop command -line option parsin g not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
14 /09/02 11:14:04 INFO mapred.FileInputFormat: Total input paths to process : 1
14 /09/02 11:14:04 INFO mapreduce.JobSubmitter: number of splits:2
14 /09/02 11:14:05 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_14 09386620927_0015
14 /09/02 11:14:05 INFO impl.YarnClientImpl: Submitted application application_14 09386620927_0015
14 /09/02 11:14:05 INFO mapreduce.Job: The url to track the job: http: //hadoop :80 88 /proxy/application_1409386620927_0015/
14 /09/02 11:14:05 INFO mapreduce.Job: Running job: job_1409386620927_0015
14 /09/02 11:14:12 INFO mapreduce.Job: Job job_1409386620927_0015 running in uber mode : false
14 /09/02 11:14:12 INFO mapreduce.Job: map 0% reduce 0%
14 /09/02 11:14:21 INFO mapreduce.Job: map 100% reduce 0%
14 /09/02 11:14:28 INFO mapreduce.Job: map 100% reduce 100%
14 /09/02 11:14:28 INFO mapreduce.Job: Job job_1409386620927_0015 completed successfully
14 /09/02 11:14:29 INFO mapreduce.Job: Counters: 49
File System Counters
FILE: Number of bytes read =105
FILE: Number of bytes written=289816
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read =1638
HDFS: Number of bytes written=10
HDFS: Number of read operations=9
HDFS: Number of large read operations=0
HDFS: Number of write operations=2
Job Counters
Launched map tasks=2
Launched reduce tasks=1
Data- local map tasks=2
Total time spent by all maps in occupied slots (ms)=14817
Total time spent by all reduces in occupied slots (ms)=4500
Total time spent by all map tasks (ms)=14817
Total time spent by all reduce tasks (ms)=4500
Total vcore-seconds taken by all map tasks=14817
Total vcore-seconds taken by all reduce tasks=4500
Total megabyte-seconds taken by all map tasks=15172608
Total megabyte-seconds taken by all reduce tasks=4608000
Map-Reduce Framework
Map input records=9
Map output records=9
Map output bytes=81
Map output materialized bytes=111
Input split bytes=208
Combine input records=0
Combine output records=0
Reduce input groups =1
Reduce shuffle bytes=111
Reduce input records=9
Reduce output records=1
Spilled Records=18
Shuffled Maps =2
Failed Shuffles=0
Merged Map outputs=2
GC time elapsed (ms)=115
CPU time spent (ms)=1990
Physical memory (bytes) snapshot=655314944
Virtual memory (bytes) snapshot=2480295936
Total committed heap usage (bytes)=466616320
Shuffle Errors
BAD_ID=0
CONNECTION=0
IO_ERROR=0
WRONG_LENGTH=0
WRONG_MAP=0
WRONG_REDUCE=0
File Input Format Counters
Bytes Read=1430
File Output Format Counters
Bytes Written=10
a01513@hadoop :~$ |
我们可以到Eclipse查看输出的结果:
或者用命令行查看:
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$ hdfs dfs - cat output /part-00000
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假如你们发现运行后结果是为空的,可能到日志目录查找相应的log.info输出信息,log目录在:/usr/mywind/hadoop/logs/userlogs 下面。
好了,不太喜欢打字,以上就是整个过程了,欢迎大家来学习指正。