hadoop2.6安装配置以及整合eclipse开发环境

在ubuntu14.04上安装java和hadoop环境

Java安装的是/usr/lib/jvm/jdk1.7.0_72

1.下载，

2.使用sudo创建jvm文件夹，并且cp

3.解压tar–zxvf

4.sudochown -R castle:castle hadoop-2.6.0修改权限

5.配置环境变量

~/.profile中也可以在~/.bashrc中添加

#setjava env

exportJAVA_HOME=/usr/lib/jvm/jdk1.7.0_72

exportJRE_HOME=${JAVA_HOME}/jre

exportCLASSPATH=.:${JAVA_HOME}/lib:${JRE_HOME}/lib

exportPATH=${JAVA_HOME}/bin:$PATH

#sethadoop env

exportHADOOP_HOME=/usr/local/hadoop/hadoop-2.6.0

exportPATH=$PATH:$HADOOP_HOME/bin

source .profile
不需要注销登陆时文件生效

hadoop/usr/local/hadoop/hadoop-2.6.0

前面的步骤与上面的很相似的

1.配置etc/hadoop/hadoop-env.sh

#set to the root of your Java installation

exportJAVA_HOME=/usr/lib/jvm/jdk1.7.0_72

#hadoop

exportHADOOP_PREFIX=/usr/local/hadoop/hadoop-2.6.0

2.伪分布配置

etc/hadoop/core-site.xml:



<property>



        <name>hadoop.tmp.dir</name>



<value>/usr/local/hadoop/hadoop-2.6.0/tmp</value>



        <description>Abase for other
temporary directories.



          </description>



    </property>



<configuration>


   <property>


       <name>fs.defaultFS</name>


       <value>hdfs://localhost:9000</value>


   </property>


</configuration>




etc/hadoop/hdfs-site.xml:


<configuration>



   <property>



       <name>dfs.replication</name>



       <value>1</value>



   </property>



   <property>



       <name>dfs.namenode.name.dir</name>



       <value>file:/usr/local/hadoop/hadoop-2.6.0/dfs/name</value>



   </property>



   <property>



       <name>dfs.datanode.data.dir</name>



       <value>file:/usr/local/hadoop/hadoop-2.6.0/dfs/data</value>



   </property>



   <property>



           <name>dfs.permissions</name>



           <value>false</value>

////这个属性节点是为了防止后面eclopse存在拒绝读写设置的


    </property>



</configuration>


mapred-site.xml


<!--mapreduce parameter -->


<!--新框架支持第三方MapReduce开发框架以支持如SmartTalk/DGSG等非Yarn架构，注意通常情况下这个配置的值都设置为Yarn，


如果没有配置这项，那么提交的Yarn job只会运行在locale模式，而不是分布式模式。-->


<configuration>


<property>


<name>mapreduce.framework.name</name>


<value>yarn</value>


</property>


</configuration>


注意：旧版的mapreduce在这里面是要配置以下内容的：


<property>


<name>mapred.job.tracker</name>


<value>http://192.168.1.2:9001</value>


</property>


新框架中已改为Yarn-site.xml中的resouceManager及nodeManager具体配置项，新框架中历史job的查询已从Jobtracker剥离，归入单独的mapreduce.jobtracker.jobhistory相关配置，


所以这里不需要配置这个选项。在yarn-site.xml配置相关属性即可。


yarn-site.xml


<configuration>


    <property>


<name>yarn.nodemanager.aux-services</name>


        <value>mapreduce_shuffle</value>


    </property>


</configuration>


关于新旧版本的mapreduce的差别可以查看这些：


http://www.ibm.com/developerworks/cn/opensource/os-cn-hadoop-yarn/


虾皮最经典的集群配置方法。http://www.cnblogs.com/xia520pi/archive/2012/05/16/2503949.html


其他的博文


http://www.cnblogs.com/kinglau/p/3802705.html


http://blog.csdn.net/ggz631047367/article/details/42497557


3.配置SSH无密码登陆


如果ubuntu没有安装ssh相关的软件


$
sudo apt-get install ssh


$
sudo apt-get install rsync


Setuppassphraseless ssh


Nowcheck that you can ssh to the localhost without a passphrase:


 $
ssh localhost


Ifyou cannot ssh to localhost without a passphrase, execute thefollowing commands:


 $
ssh-keygen -t dsa -P ‘‘ -f ~/.ssh/id_dsa


 $
cat ~/.ssh/id_dsa.pub >> ~/.ssh/authorized_keys






ssh-keygen
代表产生密钥


ssh
localhost还是出现问题
无法连接


ssh:
connect to host localhost port 22: Connection refused


从网上得知解决办法


1.首先查看是否有sshd进程
ps
-e | grep ssh


2.没有的话启动
  /etc/init.d/ssh
-start 如果启动不了的话，需要安装


3.安装
sudo
apt-get install openssh-server


4.重新启动


5.查看可以了


1695
?        00:00:00 ssh-agent


12407
?        00:00:00 sshd


[email protected]:~$
ssh localhost



The
authenticity of host ‘localhost (127.0.0.1)‘ can‘t be established.


ECDSA
key fingerprint is ae:23:4a:95:bc:37:dd:1a:5b:48:4f:66:e2:87:12:1c.


Are
you sure you want to continue connecting (yes/no)? y


Please
type ‘yes‘ or ‘no‘: yes


Warning:
Permanently added ‘localhost‘ (ECDSA) to the list of known hosts.


Welcome
to Ubuntu 14.04 LTS (GNU/Linux 3.13.0-43-generic x86_64)




*
Documentation:  https://help.ubuntu.com/






The
programs included with the Ubuntu system are free software;


the
exact distribution terms for each program are described in the


individual
files in /usr/share/doc/*/copyright.




Ubuntu
comes with ABSOLUTELY NO WARRANTY, to the extent permitted by


applicable
law.


















 $
bin/hdfs namenode -format


bin/hdfs namenode -format 只需要执行一次即可。如果执行两次的话，



每次namenode
format会重新创建一个namenodeId



/usr/local/hadoop/hadoop2.6.0/tmp/dfs/name
会被清空;而datanode不清空。



会出现：datanode的clusterID
和
namenode的clusterID
不匹配



出现这种问题的解决办法是：修改.../tmp/dfs/name下的namenodeId.



为什么我在hadoop0.20.2中每一次都执行了format？


我想是因为我每一次format都不成功的原因吧。




hdfs dfs -mkdir /user 在hdfs中创建文件夹。






$
sbin/start-dfs.sh


使用jps命令查看


2855
org.eclipse.equinox.launcher_1.3.0.v20140415-2008.jar



11127 DataNode



10975 NameNode



11432 Jps



11284 SecondaryNameNode



表示成功了。




$
sbin/start-yarn.sh




$
sbin/stop-dfs.sh




$
sbin/stop-yarn.sh








如果在eclipse运行helloword的时候，控制台没有打印出运行的过程。那么就将hadoop安装文件夹中的etc/hadoop/log4j.properties复制到eclipse项目中的src文件夹中即可。




15/01/2410:30:12 WARN util.NativeCodeLoader: Unable to load native-hadooplibrary for your platform...

 using builtin-java classes whereapplicable


15/01/2410:30:13 INFO Configuration.deprecation: session.id is deprecated.Instead, use dfs.metrics.session-id


15/01/2410:30:13 INFO jvm.JvmMetrics: Initializing JVM Metrics withprocessName=JobTracker,

 sessionId=


15/01/2410:30:13 WARN mapreduce.JobSubmitter: No job jar file set. Userclasses may not be

 found. See Job or Job#setJar(String).


15/01/2410:30:13 INFO input.FileInputFormat: Total input paths to process : 2


15/01/2410:30:14 INFO mapreduce.JobSubmitter: number of splits:2


15/01/2410:30:14 INFO mapreduce.JobSubmitter: Submitting tokens for job:job_local632218717_0001


15/01/2410:30:14 INFO mapreduce.Job: The url to track the job:http://localhost:8080/


15/01/2410:30:14 INFO mapreduce.Job: Running job: job_local632218717_0001


15/01/2410:30:14 INFO mapred.LocalJobRunner: OutputCommitter set in confignull


15/01/2410:30:14 INFO mapred.LocalJobRunner: OutputCommitter isorg.apache.hadoop.mapreduce.lib.output.FileOutputCommitter


15/01/2410:30:15 INFO mapred.LocalJobRunner: Waiting for map tasks


15/01/2410:30:15 INFO mapred.LocalJobRunner: Starting task:attempt_local632218717_0001_m_000000_0


15/01/2410:30:15 INFO mapred.Task: Using ResourceCalculatorProcessTree : [ ]


15/01/2410:30:15 INFO mapred.MapTask: Processing split:hdfs://localhost:9000/user/castle/wordcount_input/input1:0+32


15/01/2410:30:15 INFO mapred.MapTask: (EQUATOR) 0 kvi 26214396(104857584)


15/01/2410:30:15 INFO mapred.MapTask: mapreduce.task.io.sort.mb: 100


15/01/2410:30:15 INFO mapred.MapTask: soft limit at 83886080


15/01/2410:30:15 INFO mapred.MapTask: bufstart = 0; bufvoid = 104857600


15/01/2410:30:15 INFO mapred.MapTask: kvstart = 26214396; length = 6553600


15/01/2410:30:15 INFO mapred.MapTask: Map output collector class =org.apache.hadoop.mapred.MapTask$MapOutputBuffer


15/01/2410:30:15 INFO mapred.LocalJobRunner:


15/01/2410:30:15 INFO mapred.MapTask: Starting flush of map output


15/01/2410:30:15 INFO mapred.MapTask: Spilling map output


15/01/2410:30:15 INFO mapred.MapTask: bufstart = 0; bufend = 52; bufvoid =104857600


15/01/2410:30:15 INFO mapred.MapTask: kvstart = 26214396(104857584); kvend =26214380(104857520);

 length = 17/6553600


15/01/2410:30:15 INFO mapred.MapTask: Finished spill 0


15/01/2410:30:15 INFO mapred.Task:Task:attempt_local632218717_0001_m_000000_0 is done. And

 is in theprocess of committing


15/01/2410:30:15 INFO mapred.LocalJobRunner: map


15/01/2410:30:15 INFO mapred.Task: Task‘attempt_local632218717_0001_m_000000_0‘ done.


15/01/2410:30:15 INFO mapred.LocalJobRunner: Finishing task:attempt_local632218717_0001_m_000000_0


15/01/2410:30:15 INFO mapred.LocalJobRunner: Starting task:attempt_local632218717_0001_m_000001_0


15/01/2410:30:15 INFO mapred.Task: Using ResourceCalculatorProcessTree : [ ]


15/01/2410:30:15 INFO mapred.MapTask: Processing split:hdfs://localhost:9000/user/castle/wordcount_input/input2:0+29


15/01/2410:30:15 INFO mapred.MapTask: (EQUATOR) 0 kvi 26214396(104857584)


15/01/2410:30:15 INFO mapred.MapTask: mapreduce.task.io.sort.mb: 100


15/01/2410:30:15 INFO mapred.MapTask: soft limit at 83886080


15/01/2410:30:15 INFO mapred.MapTask: bufstart = 0; bufvoid = 104857600


15/01/2410:30:15 INFO mapred.MapTask: kvstart = 26214396; length = 6553600


15/01/2410:30:15 INFO mapred.MapTask: Map output collector class =org.apache.hadoop.mapred.MapTask$MapOutputBuffer


15/01/2410:30:15 INFO mapred.LocalJobRunner:


15/01/2410:30:15 INFO mapred.MapTask: Starting flush of map output


15/01/2410:30:15 INFO mapred.MapTask: Spilling map output


15/01/2410:30:15 INFO mapred.MapTask: bufstart = 0; bufend = 49; bufvoid =104857600


15/01/2410:30:15 INFO mapred.MapTask: kvstart = 26214396(104857584); kvend =26214380(104857520);

 length = 17/6553600


15/01/2410:30:15 INFO mapred.MapTask: Finished spill 0


15/01/2410:30:15 INFO mapred.Task:Task:attempt_local632218717_0001_m_000001_0 is done. And

 is in theprocess of committing


15/01/2410:30:15 INFO mapred.LocalJobRunner: map


15/01/2410:30:15 INFO mapred.Task: Task‘attempt_local632218717_0001_m_000001_0‘ done.


15/01/2410:30:15 INFO mapred.LocalJobRunner: Finishing task:attempt_local632218717_0001_m_000001_0


15/01/2410:30:15 INFO mapred.LocalJobRunner: map task executor complete.


15/01/2410:30:15 INFO mapred.LocalJobRunner: Waiting for reduce tasks


15/01/2410:30:15 INFO mapred.LocalJobRunner: Starting task:attempt_local632218717_0001_r_000000_0


15/01/2410:30:15 INFO mapred.Task: Using ResourceCalculatorProcessTree : [ ]


15/01/2410:30:15 INFO mapred.ReduceTask: Using ShuffleConsumerPlugin:[email protected]


15/01/2410:30:15 INFO reduce.MergeManagerImpl: MergerManager:memoryLimit=626471744, maxSingleShuffleLimit=156617936,mergeThreshold=413471360,

 ioSortFactor=10,memToMemMergeOutputsThreshold=10


15/01/2410:30:15 INFO reduce.EventFetcher:attempt_local632218717_0001_r_000000_0 Thread

 started: EventFetcherfor fetching Map Completion Events


15/01/2410:30:15 INFO mapreduce.Job: Job job_local632218717_0001 running inuber mode : false


15/01/2410:30:15 INFO mapreduce.Job: map 100% reduce 0%


15/01/2410:30:16 INFO reduce.LocalFetcher: localfetcher#1 about to shuffleoutput of map

 attempt_local632218717_0001_m_000000_0 decomp: 40 len:44 to MEMORY


15/01/2410:30:16 INFO reduce.InMemoryMapOutput: Read 40 bytes from map-outputfor attempt_local632218717_0001_m_000000_0


15/01/2410:30:16 INFO reduce.MergeManagerImpl: closeInMemoryFile ->map-output of size: 40,

 inMemoryMapOutputs.size() -> 1,commitMemory -> 0, usedMemory ->40


15/01/2410:30:16 INFO reduce.LocalFetcher: localfetcher#1 about to shuffleoutput of map

 attempt_local632218717_0001_m_000001_0 decomp: 51 len:55 to MEMORY


15/01/2410:30:16 INFO reduce.InMemoryMapOutput: Read 51 bytes from map-outputfor attempt_local632218717_0001_m_000001_0


15/01/2410:30:16 INFO reduce.MergeManagerImpl: closeInMemoryFile ->map-output of size: 51,

 inMemoryMapOutputs.size() -> 2,commitMemory -> 40, usedMemory ->91


15/01/2410:30:16 INFO reduce.EventFetcher: EventFetcher is interrupted..Returning


15/01/2410:30:16 INFO mapred.LocalJobRunner: 2 / 2 copied.


15/01/2410:30:16 INFO reduce.MergeManagerImpl: finalMerge called with 2in-memory map-outputs

 and 0 on-disk map-outputs


15/01/2410:30:16 INFO mapred.Merger: Merging 2 sorted segments


15/01/2410:30:16 INFO mapred.Merger: Down to the last merge-pass, with 2segments left of

 total size: 71 bytes


15/01/2410:30:16 INFO reduce.MergeManagerImpl: Merged 2 segments, 91 bytes todisk to satisfy

 reduce memory limit


15/01/2410:30:16 INFO reduce.MergeManagerImpl: Merging 1 files, 93 bytes fromdisk


15/01/2410:30:16 INFO reduce.MergeManagerImpl: Merging 0 segments, 0 bytesfrom memory into

 reduce


15/01/2410:30:16 INFO mapred.Merger: Merging 1 sorted segments


15/01/2410:30:16 INFO mapred.Merger: Down to the last merge-pass, with 1segments left of

 total size: 79 bytes


15/01/2410:30:16 INFO mapred.LocalJobRunner: 2 / 2 copied.


15/01/2410:30:16 INFO Configuration.deprecation: mapred.skip.on isdeprecated. Instead, use

 mapreduce.job.skiprecords


15/01/2410:30:16 INFO mapred.Task:Task:attempt_local632218717_0001_r_000000_0 is done. And

 is in theprocess of committing


15/01/2410:30:16 INFO mapred.LocalJobRunner: 2 / 2 copied.


15/01/2410:30:16 INFO mapred.Task: Taskattempt_local632218717_0001_r_000000_0 is allowed

 to commit now


15/01/2410:30:16 INFO output.FileOutputCommitter: Saved output of task‘attempt_local632218717_0001_r_000000_0‘

 tohdfs://localhost:9000/user/castle/wordcount_output/_temporary/0/task_local632218717_0001_r_000000


15/01/2410:30:16 INFO mapred.LocalJobRunner: reduce > reduce


15/01/2410:30:16 INFO mapred.Task: Task‘attempt_local632218717_0001_r_000000_0‘ done.


15/01/2410:30:16 INFO mapred.LocalJobRunner: Finishing task:attempt_local632218717_0001_r_000000_0


15/01/2410:30:16 INFO mapred.LocalJobRunner: reduce task executor complete.


15/01/2410:30:16 INFO mapreduce.Job: map 100% reduce 100%


15/01/2410:30:16 INFO mapreduce.Job: Job job_local632218717_0001 completedsuccessfully


15/01/2410:30:16 INFO mapreduce.Job: Counters: 38


FileSystem Counters


FILE:Number of bytes read=1732


FILE:Number of bytes written=754881


FILE:Number of read operations=0


FILE:Number of large read operations=0


FILE:Number of write operations=0


HDFS:Number of bytes read=154


HDFS:Number of bytes written=42


HDFS:Number of read operations=25


HDFS:Number of large read operations=0


HDFS:Number of write operations=5


Map-ReduceFramework


Mapinput records=10


Mapoutput records=10


Mapoutput bytes=101


Mapoutput materialized bytes=99


Inputsplit bytes=242


Combineinput records=10


Combineoutput records=7


Reduceinput groups=5


Reduceshuffle bytes=99


Reduceinput records=7


Reduceoutput records=5


SpilledRecords=14


ShuffledMaps =2


FailedShuffles=0


MergedMap outputs=2


GCtime elapsed (ms)=0


CPUtime spent (ms)=0


Physicalmemory (bytes) snapshot=0


Virtualmemory (bytes) snapshot=0


Totalcommitted heap usage (bytes)=855638016


ShuffleErrors


BAD_ID=0


CONNECTION=0


IO_ERROR=0


WRONG_LENGTH=0


WRONG_MAP=0


WRONG_REDUCE=0


FileInput Format Counters


BytesRead=61


FileOutput Format Counters


BytesWritten=42




























































Hadoop2.6和eclipse整合开发配置


编译hadoop
eclipse插件


git
clone https://github.com/winghc/hadoop2x-eclipse-plugin.git


然后使用ant进行编译


 cd
src/contrib/eclipse-plugin


ant jar -Dversion=2.6.0 -Declipse.home=/usr/local/eclipse -Dhadoop.home=/usr/local/hadoop-2.6.0  //需要手动安装的eclipse，通过命令行一键安装的不行


eclipse.home和hadoop.home设置成你自己的环境路径


生成的位置是：/home/hunter/hadoop2x-eclipse-plugin/build/contrib/eclipse-plugin/hadoop-eclipse-plugin-2.6.0.jar


不好意思我没有成功，就是编译的时候卡在那里，也不报错什么的。


后来我用这个git文件中release下有一个hadoop2.2.0版本的。用这个就可以，其他的就不行。




































































右边配置的要和core-site.xml中的一致。


左边的话可以不需要配置，以前旧版的mapreduce是配置和mapred-site.xml中的一致。




				


				
时间： 2024-08-06 16:01:33 

		


		


	

	
	

		


		
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                怎样在Win7 64位旗舰版安装Python+Eclipse开发环境
			

		

		

									

				原地址:http://www.cnblogs.com/balian/archive/2011/06/19/2084632.html 自从上周抛弃了WinXP转而安装了Win7,64位后,尝试安装Python+Eclipse遇到了一点小问题.现在已经解决,将安装顺序记录如下,供参考. Setp1,到ORACLE网站下载64位的JDK.http://www.oracle.com/technetwork/java/javase/downloads/index.html下载JDK,我下载的是jdk-6u