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*。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

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

[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）

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

[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

ssh-keygen -t rsa 

拷贝ssh密钥到三台机器

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

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

<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

<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

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:

java -version 

可以将当前节点中设置的文件拷贝到其他节点

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

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）

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

<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

<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

<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

<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）

 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

[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

[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

[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

[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：

[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

[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

hdfs上传文件

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

运行wordcount程序

[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

查看最后的词频统计结果

浏览器中查看hdfs的文件系统

[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 
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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。