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

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
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

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