Hadoop2.6安装配置以及整合Eclipse开发环境

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：

虾皮最经典的集群配置方法。

其他的修改文章

 

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

 $
ssh localhostIfyou 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_keysssh-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 ssh2.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-server4.重新启动5.查看可以了1695
?        00:00:00 ssh-agent12407
?        00:00:00 sshdcastle@castle-X550VC:~$
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)? yPlease
type 'yes' or 'no': yesWarning:
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 theindividual
files in /usr/share/doc/*/copyright.Ubuntu
comes with ABSOLUTELY NO WARRANTY, to the extent permitted byapplicable
law. $
bin/hdfs namenode -formatbin/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:org.apache.hadoop.mapreduce.task.reduce.Shuffle@158e338a

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-pluginant 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。

CentOS安装和配置Hadoop2.2.0 

Ubuntu 13.04上搭建Hadoop环境

Ubuntu 12.10 +Hadoop 1.2.1版本集群配置

Ubuntu上搭建Hadoop环境（单机模式+伪分布模式）

Ubuntu下Hadoop环境的配置

单机版搭建Hadoop环境图文教程详解

搭建Hadoop环境（在Winodws环境下用虚拟机虚拟两个Ubuntu系统进行搭建）