基于CentOS的Hadoop分布式环境的搭建开发

首先,要说明的一点的是,我不想重复发明轮子。如果想要搭建hadoop环境,网上有很多详细的步骤和命令代码,我不想再重复记录。

其次,我要说的是我也是新手,对于hadoop也不是很熟悉。但是就是想实际搭建好环境,看看他的庐山真面目,还好,还好,最好看到了。当运行wordcount词频统计的时候,实在是感叹hadoop已经把分布式做的如此之好,即使没有分布式相关经验的人,也只需要做一些配置即可运行分布式集群环境。

好了,言归真传。

在搭建hadoop环境中你要知道的一些事儿:

1.hadoop运行于linux系统之上,你要安装linux操作系统

2.你需要搭建一个运行hadoop的集群,例如局域网内能互相访问的linux系统

3.为了实现集群之间的相互访问,你需要做到ssh无密钥登录

4.hadoop的运行在jvm上的,也就是说你需要安装java的jdk,并配置好java_home

5.hadoop的各个组件是通过xml来配置的。在官网上下载好hadoop之后解压缩,修改/etc/hadoop目录中相应的配置文件

工欲善其事,必先利其器。这里也要说一下,在搭建hadoop环境中使用到的相关软件和工具:

1.virtualbox——毕竟要模拟几台linux,条件有限,就在virtualbox中创建几台虚拟机楼

2.centos——下载的centos7的iso镜像,加载到virtualbox中,安装运行

3.securecrt——可以ssh远程访问linux的软件

4.winscp——实现windows和linux的通信

5.jdk for linux——oracle官网上下载,解压缩之后配置一下即可

6.hadoop2.7.1——可在apache官网上下载

好了,下面分三个步骤来讲解

linux环境准备

 配置ip

为了实现本机和虚拟机以及虚拟机和虚拟机之间的通信,virtualbox中设置centos的连接模式为host-only模式,并且手动设置ip,注意虚拟机的网关和本机中host-only network 的ip地址相同。配置ip完成后还要重启网络服务以使得配置有效。这里搭建了三台linux,如下图所示

基于CentOS的Hadoop分布式环境的搭建开发

基于CentOS的Hadoop分布式环境的搭建开发

基于CentOS的Hadoop分布式环境的搭建开发

基于CentOS的Hadoop分布式环境的搭建开发

配置主机名字

对于192.168.56.101设置主机名字hadoop01。并在hosts文件中配置集群的ip和主机名。其余两个主机的操作与此类似

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[root@hadoop01 ~]# cat /etc/sysconfig/network
# created by anaconda
networking = yes
hostname = hadoop01  
[root@hadoop01 ~]# cat /etc/hosts
127.0.0.1  localhost localhost.localdomain localhost4 localhost4.localdomain4
::1     localhost localhost.localdomain localhost6 localhost6.localdomain6
192.168.56.101 hadoop01
192.168.56.102 hadoop02
192.168.56.103 hadoop03

永久关闭防火墙

service iptables stop(1.下次重启机器后,防火墙又会启动,故需要永久关闭防火墙的命令;2由于用的是centos 7,关闭防火墙的命令如下)

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systemctl stop firewalld.service    #停止firewall
systemctl disable firewalld.service #禁止firewall开机启动

关闭selinux防护系统

改为disabled 。reboot重启机器,使配置生效

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[root@hadoop02 ~]# cat /etc/sysconfig/selinux
 
# this file controls the state of selinux on the system
# selinux= can take one of these three values:
#   enforcing - selinux security policy is enforced
 
#   permissive - selinux prints warnings instead of enforcing
#   disabled - no selinux policy is loaded
selinux=disabled
# selinuxtype= can take one of three two values:
#   targeted - targeted processes are protected,
#   minimum - modification of targeted policy only selected processes are protected
#   mls - multi level security protection
selinuxtype=targeted

集群ssh免密码登录

首先设置ssh密钥

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ssh-keygen -t rsa

拷贝ssh密钥到三台机器

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ssh-copy-id 192.168.56.101
<pre name="code" class="plain">ssh-copy-id 192.168.56.102

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ssh-copy-id 192.168.56.103

这样如果hadoop01的机器想要登录hadoop02,直接输入ssh hadoop02

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<pre name="code" class="plain">ssh hadoop02

配置jdk

这里在/home忠诚创建三个文件夹中

tools——存放工具包

softwares——存放软件

data——存放数据

通过winscp将下载好的linux jdk上传到hadoop01的/home/tools中

解压缩jdk到softwares中

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<pre name="code" class="plain">tar -zxf jdk-7u76-linux-x64.tar.gz -c /home/softwares

可见jdk的家目录在/home/softwares/jdk.x.x.x,将该目录拷贝粘贴到/etc/profile文件中,并且在文件中设置java_home

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export java_home=/home/softwares/jdk0_111
export path=$path:$java_home/bin

保存修改,执行source /etc/profile使配置生效

查看java jdk是否安装成功:

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java -version

可以将当前节点中设置的文件拷贝到其他节点

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scp -r /home/* root@192.168.56.10x:/home

hadoop集群安装

集群的规划如下:

101节点作为hdfs的namenode ,其余作为datanode;102作为yarn的resourcemanager,其余作为nodemanager。103作为secondarynamenode。分别在101和102节点启动jobhistoryserver和webappproxyserver基于CentOS的Hadoop分布式环境的搭建开发

下载hadoop-2.7.3

并将其放在/home/softwares文件夹中。由于hadoop需要jdk的安装环境,所以首先配置/etc/hadoop/hadoop-env.sh的java_home

(ps:感觉我用的jdk版本过高了)基于CentOS的Hadoop分布式环境的搭建开发

接下来依次修改hadoop相应组件对应的xml

修改core-site.xml :

指定namenode地址

修改hadoop的缓存目录

hadoop的垃圾回收机制

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<configuration>
  <property>
    <name>fsdefaultfs</name>
    <value>hdfs://101:8020</value>
  </property>
  <property>
    <name>hadooptmpdir</name>
    <value>/home/softwares/hadoop-3/data/tmp</value>
  </property>
  <property>
    <name>fstrashinterval</name>
    <value>10080</value>
  </property>
   
</configuration>

hdfs-site.xml

设置备份数目

关闭权限

设置http访问接口

设置secondary namenode 的ip地址

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<configuration>
  <property>
    <name>dfsreplication</name>
    <value>3</value>
  </property>
  <property>
    <name>dfspermissionsenabled</name>
    <value>false</value>
  </property>
  <property>
    <name>dfsnamenodehttp-address</name>
    <value>101:50070</value>
  </property>
  <property>
    <name>dfsnamenodesecondaryhttp-address</name>
    <value>103:50090</value>
  </property>
</configuration>

 修改mapred-site.xml.template名字为mapred-site.xml

指定mapreduce的框架为yarn,通过yarn来调度

指定jobhitory

指定jobhitory的web端口

开启uber模式——这是针对mapreduce的优化

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<configuration>
  <property>
    <name>mapreduceframeworkname</name>
    <value>yarn</value>
  </property>
  <property>
    <name>mapreducejobhistoryaddress</name>
    <value>101:10020</value>
  </property>
  <property>
    <name>mapreducejobhistorywebappaddress</name>
    <value>101:19888</value>
  </property>
  <property>
    <name>mapreducejobubertaskenable</name>
    <value>true</value>
  </property>
</configuration>

修改yarn-site.xml

指定mapreduce为shuffle

指定102节点为resourcemanager

指定102节点的安全代理

开启yarn的日志

指定yarn日志删除时间

指定nodemanager的内存:8g

指定nodemanager的cpu:8核

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<configuration>
 
<!-- site specific yarn configuration properties -->
  <property>
    <name>yarnnodemanageraux-services</name>
    <value>mapreduce_shuffle</value>
  </property>
  <property>
    <name>yarnresourcemanagerhostname</name>
    <value>102</value>
  </property>
  <property>
    <name>yarnweb-proxyaddress</name>
    <value>102:8888</value>
  </property>
  <property>
    <name>yarnlog-aggregation-enable</name>
    <value>true</value>
  </property>
  <property>
    <name>yarnlog-aggregationretain-seconds</name>
    <value>604800</value>
  </property>
  <property>
    <name>yarnnodemanagerresourcememory-mb</name>
    <value>8192</value>
  </property>
  <property>
    <name>yarnnodemanagerresourcecpu-vcores</name>
    <value>8</value>
  </property>
 
</configuration>

配置slaves

指定计算节点,即运行datanode和nodemanager的节点

192.168.56.101 
192.168.56.102 
192.168.56.103 

先在namenode节点格式化,即101节点上执行:

进入到hadoop主目录: cd /home/softwares/hadoop-3  

执行bin目录下的hadoop脚本: bin/hadoop namenode -format 

出现successful format才算是执行成功(ps,这里是盗用别人的图,不要介意哈) 基于CentOS的Hadoop分布式环境的搭建开发

 以上配置完成后,将其拷贝到其他的机器

hadoop环境测试

进入hadoop主目录下执行相应的脚本文件

jps命令——java virtual machine process status,显示运行的java进程

在namenode节点101机器上开启hdfs

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[root@hadoop01 hadoop-3]# sbin/start-dfssh 
java hotspot(tm) client vm warning: you have loaded library /home/softwares/hadoop-3/lib/native/libhadoopso which might have disabled stack guard the vm will try to fix the stack guard now
it's highly recommended that you fix the library with 'execstack -c <libfile>', or link it with '-z noexecstack'
16/11/07 16:49:19 warn utilnativecodeloader: unable to load native-hadoop library for your platform using builtin-java classes where applicable
starting namenodes on [hadoop01]
hadoop01: starting namenode, logging to /home/softwares/hadoop-3/logs/hadoop-root-namenode-hadoopout
102: starting datanode, logging to /home/softwares/hadoop-3/logs/hadoop-root-datanode-hadoopout
103: starting datanode, logging to /home/softwares/hadoop-3/logs/hadoop-root-datanode-hadoopout
101: starting datanode, logging to /home/softwares/hadoop-3/logs/hadoop-root-datanode-hadoopout
starting secondary namenodes [hadoop03]
hadoop03: starting secondarynamenode, logging to /home/softwares/hadoop-3/logs/hadoop-root-secondarynamenode-hadoopout

此时101节点上执行jps,可以看到namenode和datanode已经启动

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[root@hadoop01 hadoop-3]# jps
7826 jps
7270 datanode
7052 namenode

在102和103节点执行jps,则可以看到datanode已经启动

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[root@hadoop02 bin]# jps
4260 datanode
4488 jps
 
[root@hadoop03 ~]# jps
6436 secondarynamenode
6750 jps
6191 datanode

启动yarn

在102节点执行

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[root@hadoop02 hadoop-3]# sbin/start-yarnsh 
starting yarn daemons
starting resourcemanager, logging to /home/softwares/hadoop-3/logs/yarn-root-resourcemanager-hadoopout
101: starting nodemanager, logging to /home/softwares/hadoop-3/logs/yarn-root-nodemanager-hadoopout
103: starting nodemanager, logging to /home/softwares/hadoop-3/logs/yarn-root-nodemanager-hadoopout
102: starting nodemanager, logging to /home/softwares/hadoop-3/logs/yarn-root-nodemanager-hadoopout

jps查看各节点:

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[root@hadoop02 hadoop-3]# jps
4641 resourcemanager
4260 datanode
4765 nodemanager
5165 jps
 
 
[root@hadoop01 hadoop-3]# jps
7270 datanode
8375 jps
7976 nodemanager
7052 namenode
 
 
[root@hadoop03 ~]# jps
6915 nodemanager
6436 secondarynamenode
7287 jps
6191 datanode

分别启动相应节点的jobhistory和防护进程

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[root@hadoop01 hadoop-3]# sbin/mr-jobhistory-daemonsh start historyserver
starting historyserver, logging to /home/softwares/hadoop-3/logs/mapred-root-historyserver-hadoopout
[root@hadoop01 hadoop-3]# jps
8624 jps
7270 datanode
7976 nodemanager
8553 jobhistoryserver
7052 namenode
 
[root@hadoop02 hadoop-3]# sbin/yarn-daemonsh start proxyserver
starting proxyserver, logging to /home/softwares/hadoop-3/logs/yarn-root-proxyserver-hadoopout
[root@hadoop02 hadoop-3]# jps
4641 resourcemanager
4260 datanode
5367 webappproxyserver
5402 jps
4765 nodemanager

在hadoop01节点,即101节点上,通过浏览器查看节点状况 基于CentOS的Hadoop分布式环境的搭建开发基于CentOS的Hadoop分布式环境的搭建开发

hdfs上传文件

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[root@hadoop01 hadoop-3]# bin/hdfs dfs -put /etc/profile /profile

运行wordcount程序

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[root@hadoop01 hadoop-3]# bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-jar wordcount /profile /fll_out
java hotspot(tm) client vm warning: you have loaded library /home/softwares/hadoop-3/lib/native/libhadoopso which might have disabled stack guard the vm will try to fix the stack guard now
it's highly recommended that you fix the library with 'execstack -c <libfile>', or link it with '-z noexecstack'
16/11/07 17:17:10 warn utilnativecodeloader: unable to load native-hadoop library for your platform using builtin-java classes where applicable
16/11/07 17:17:12 info clientrmproxy: connecting to resourcemanager at /102:8032
16/11/07 17:17:18 info inputfileinputformat: total input paths to process : 1
16/11/07 17:17:19 info mapreducejobsubmitter: number of splits:1
16/11/07 17:17:19 info mapreducejobsubmitter: submitting tokens for job: job_1478509135878_0001
16/11/07 17:17:20 info implyarnclientimpl: submitted application application_1478509135878_0001
16/11/07 17:17:20 info mapreducejob: the url to track the job: http://102:8888/proxy/application_1478509135878_0001/
16/11/07 17:17:20 info mapreducejob: running job: job_1478509135878_0001
16/11/07 17:18:34 info mapreducejob: job job_1478509135878_0001 running in uber mode : true
16/11/07 17:18:35 info mapreducejob: map 0% reduce 0%
16/11/07 17:18:43 info mapreducejob: map 100% reduce 0%
16/11/07 17:18:50 info mapreducejob: map 100% reduce 100%
16/11/07 17:18:55 info mapreducejob: job job_1478509135878_0001 completed successfully
16/11/07 17:18:59 info mapreducejob: counters: 52
    file system counters
        file: number of bytes read=4264
        file: number of bytes written=6412
        file: number of read operations=0
        file: number of large read operations=0
        file: number of write operations=0
        hdfs: number of bytes read=3940
        hdfs: number of bytes written=261673
        hdfs: number of read operations=35
        hdfs: number of large read operations=0
        hdfs: number of write operations=8
    job counters 
        launched map tasks=1
        launched reduce tasks=1
        other local map tasks=1
        total time spent by all maps in occupied slots (ms)=8246
        total time spent by all reduces in occupied slots (ms)=7538
        total_launched_ubertasks=2
        num_uber_submaps=1
        num_uber_subreduces=1
        total time spent by all map tasks (ms)=8246
        total time spent by all reduce tasks (ms)=7538
        total vcore-milliseconds taken by all map tasks=8246
        total vcore-milliseconds taken by all reduce tasks=7538
        total megabyte-milliseconds taken by all map tasks=8443904
        total megabyte-milliseconds taken by all reduce tasks=7718912
    map-reduce framework
        map input records=78
        map output records=256
        map output bytes=2605
        map output materialized bytes=2116
        input split bytes=99
        combine input records=256
        combine output records=156
        reduce input groups=156
        reduce shuffle bytes=2116
        reduce input records=156
        reduce output records=156
        spilled records=312
        shuffled maps =1
        failed shuffles=0
        merged map outputs=1
        gc time elapsed (ms)=870
        cpu time spent (ms)=1970
        physical memory (bytes) snapshot=243326976
        virtual memory (bytes) snapshot=2666557440
        total committed heap usage (bytes)=256876544
    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=1829
    file output format counters 
        bytes written=1487

浏览器中通过yarn查看运行状态 基于CentOS的Hadoop分布式环境的搭建开发

查看最后的词频统计结果

浏览器中查看hdfs的文件系统基于CentOS的Hadoop分布式环境的搭建开发

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[root@hadoop01 hadoop-3]# bin/hdfs dfs -cat /fll_out/part-r-00000
java hotspot(tm) client vm warning: you have loaded library /home/softwares/hadoop-3/lib/native/libhadoopso which might have disabled stack guard the vm will try to fix the stack guard now
it's highly recommended that you fix the library with 'execstack -c <libfile>', or link it with '-z noexecstack'
16/11/07 17:29:17 warn utilnativecodeloader: unable to load native-hadoop library for your platform using builtin-java classes where applicable
!=   1
"$-"  1
"$2"  1
"$euid" 2
"$histcontrol" 1
"$i"  3
"${-#*i}"    1
"0"   1
":${path}:"   1
"`id  2
"after" 1
"ignorespace"  1
#    13
$uid  1
&&   1
()   1
*)   1
*:"$1":*)    1
-f   1
-gn`"  1
-gt   1
-r   1
-ru`  1
-u`   1
-un`"  2
-x   1
-z   1
    2
/etc/bashrc   1
/etc/profile  1
/etc/profiled/ 1
/etc/profiled/*sh   1
/usr/bin/id   1
/usr/local/sbin 2
/usr/sbin    2
/usr/share/doc/setup-*/uidgid  1
002   1
022   1
199   1
200   1
2>/dev/null`  1
;    3
;;   1
=    4
>/dev/null   1
by   1
current 1
euid=`id    1
functions    1
histcontrol   1
histcontrol=ignoreboth 1
histcontrol=ignoredups 1
histsize    1
histsize=1000  1
hostname    1
hostname=`/usr/bin/hostname   1
it's  2
java_home=/home/softwares/jdk0_111 1
logname 1
logname=$user  1
mail  1
mail="/var/spool/mail/$user"  1
not   1
path  1
path=$1:$path  1
path=$path:$1  1
path=$path:$java_home/bin    1
path  1
system 1
this  1
uid=`id 1
user  1
user="`id    1
you   1
[    9
]    3
];   6
a    2
after  2
aliases 1
and   2
are   1
as   1
better 1
case  1
change 1
changes 1
check  1
could  1
create 1
custom 1
customsh    1
default,    1
do   1
doing 1
done  1
else  5
environment   1
environment,  1
esac  1
export 5
fi   8
file  2
for   5
future 1
get   1
go   1
good  1
i    2
idea  1
if   8
in   6
is   1
it   1
know  1
ksh   1
login  2
make  1
manipulation  1
merging 1
much  1
need  1
pathmunge    6
prevent 1
programs,    1
reservation   1
reserved    1
script 1
set  1
sets  1
setup  1
shell  2
startup 1
system 1
the   1
then  8
this  2
threshold    1
to   5
uid/gids    1
uidgid 1
umask  3
unless 1
unset  2
updates    1
validity    1
want  1
we   1
what  1
wide  1
will  1
workaround   1
you   2
your  1
{    1
}    1

这就代表hadoop集群正确

以上就是本文的全部内容,希望对大家的学习有所帮助,也希望大家多多支持服务器之家。

原文链接:http://blog.csdn.net/fffllllll/article/details/53066073

未经允许不得转载:VPS服务器 » 基于CentOS的Hadoop分布式环境的搭建开发

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