基于CentOS的Hadoop分布式环境的搭建——你要知道自己到底该做哪些事儿

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原创最新推荐文章于 2026-03-10 13:54:19 发布 4k 阅读 AI 写同款· GEO 优化 ›

Hadoop完全分布式环境搭建最近在学习hadoop，经过几番折腾终于把分布式环境搭建成功，这里总结一下搭建过程和过程中遇到的问题与解决方案。一、准备工作 1.下载vmvare软件，配置NAT网络模式 NAT是网络地址转换，是在宿主机和虚拟机之间增加一个地址转换服务，负责外部和虚拟机之间的通讯转接和IP转换。 2.安装centos虚拟机安装步骤这里不细讲，可以参考https://blog.csdn....... 阅读详情

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/jdk1.8.0_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>fs.defaultFS</name>
		<value>hdfs://192.168.56.101:8020</value>
	</property>
	<property>
		<name>hadoop.tmp.dir</name>
		<value>/home/softwares/hadoop-2.7.3/data/tmp</value>
	</property>
	<property>
		<name>fs.trash.interval</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>dfs.replication</name>
		<value>3</value>
	</property>
	<property>
		<name>dfs.permissions.enabled</name>
		<value>false</value>
	</property>
	<property>
		<name>dfs.namenode.http-address</name>
		<value>192.168.56.101:50070</value>
	</property>
	<property>
		<name>dfs.namenode.secondary.http-address</name>
		<value>192.168.56.103:50090</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>mapreduce.framework.name</name>
		<value>yarn</value>
	</property>
	<property>
		<name>mapreduce.jobhistory.address</name>
		<value>192.168.56.101:10020</value>
	</property>
	<property>
		<name>mapreduce.jobhistory.webapp.address</name>
		<value>192.168.56.101:19888</value>
	</property>
	<property>
		<name>mapreduce.job.ubertask.enable</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>yarn.nodemanager.aux-services</name>
		<value>mapreduce_shuffle</value>
	</property>
	<property>
		<name>yarn.resourcemanager.hostname</name>
		<value>192.168.56.102</value>
	</property>
	<property>
		<name>yarn.web-proxy.address</name>
		<value>192.168.56.102:8888</value>
	</property>
	<property>
		<name>yarn.log-aggregation-enable</name>
		<value>true</value>
	</property>
	<property>
		<name>yarn.log-aggregation.retain-seconds</name>
		<value>604800</value>
	</property>
	<property>
		<name>yarn.nodemanager.resource.memory-mb</name>
		<value>8192</value>
	</property>
	<property>
		<name>yarn.nodemanager.resource.cpu-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-2.7.3

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-2.7.3]# sbin/start-dfs.sh 
Java HotSpot(TM) Client VM warning: You have loaded library /home/softwares/hadoop-2.7.3/lib/native/libhadoop.so 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 util.NativeCodeLoader: 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-2.7.3/logs/hadoop-root-namenode-hadoop01.out
192.168.56.102: starting datanode, logging to /home/softwares/hadoop-2.7.3/logs/hadoop-root-datanode-hadoop02.out
192.168.56.103: starting datanode, logging to /home/softwares/hadoop-2.7.3/logs/hadoop-root-datanode-hadoop03.out
192.168.56.101: starting datanode, logging to /home/softwares/hadoop-2.7.3/logs/hadoop-root-datanode-hadoop01.out
Starting secondary namenodes [hadoop03]
hadoop03: starting secondarynamenode, logging to /home/softwares/hadoop-2.7.3/logs/hadoop-root-secondarynamenode-hadoop03.out

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

[root@hadoop01 hadoop-2.7.3]# 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-2.7.3]# sbin/start-yarn.sh 
starting yarn daemons
starting resourcemanager, logging to /home/softwares/hadoop-2.7.3/logs/yarn-root-resourcemanager-hadoop02.out
192.168.56.101: starting nodemanager, logging to /home/softwares/hadoop-2.7.3/logs/yarn-root-nodemanager-hadoop01.out
192.168.56.103: starting nodemanager, logging to /home/softwares/hadoop-2.7.3/logs/yarn-root-nodemanager-hadoop03.out
192.168.56.102: starting nodemanager, logging to /home/softwares/hadoop-2.7.3/logs/yarn-root-nodemanager-hadoop02.out

jps查看各节点：

[root@hadoop02 hadoop-2.7.3]# jps
4641 ResourceManager
4260 DataNode
4765 NodeManager
5165 Jps


[root@hadoop01 hadoop-2.7.3]# 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-2.7.3]# sbin/mr-jobhistory-daemon.sh start historyserver
starting historyserver, logging to /home/softwares/hadoop-2.7.3/logs/mapred-root-historyserver-hadoop01.out
[root@hadoop01 hadoop-2.7.3]# jps
8624 Jps
7270 DataNode
7976 NodeManager
8553 JobHistoryServer
7052 NameNode

[root@hadoop02 hadoop-2.7.3]# sbin/yarn-daemon.sh start proxyserver
starting proxyserver, logging to /home/softwares/hadoop-2.7.3/logs/yarn-root-proxyserver-hadoop02.out
[root@hadoop02 hadoop-2.7.3]# 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-2.7.3]# bin/hdfs dfs -put /etc/profile /profile

运行wordcount程序

[root@hadoop01 hadoop-2.7.3]# bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.3.jar wordcount /profile /fll_out
Java HotSpot(TM) Client VM warning: You have loaded library /home/softwares/hadoop-2.7.3/lib/native/libhadoop.so 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 util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
16/11/07 17:17:12 INFO client.RMProxy: Connecting to ResourceManager at /192.168.56.102:8032
16/11/07 17:17:18 INFO input.FileInputFormat: Total input paths to process : 1
16/11/07 17:17:19 INFO mapreduce.JobSubmitter: number of splits:1
16/11/07 17:17:19 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1478509135878_0001
16/11/07 17:17:20 INFO impl.YarnClientImpl: Submitted application application_1478509135878_0001
16/11/07 17:17:20 INFO mapreduce.Job: The url to track the job: http://192.168.56.102:8888/proxy/application_1478509135878_0001/
16/11/07 17:17:20 INFO mapreduce.Job: Running job: job_1478509135878_0001
16/11/07 17:18:34 INFO mapreduce.Job: Job job_1478509135878_0001 running in uber mode : true
16/11/07 17:18:35 INFO mapreduce.Job:  map 0% reduce 0%
16/11/07 17:18:43 INFO mapreduce.Job:  map 100% reduce 0%
16/11/07 17:18:50 INFO mapreduce.Job:  map 100% reduce 100%
16/11/07 17:18:55 INFO mapreduce.Job: Job job_1478509135878_0001 completed successfully
16/11/07 17:18:59 INFO mapreduce.Job: 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-2.7.3]# bin/hdfs dfs -cat /fll_out/part-r-00000
Java HotSpot(TM) Client VM warning: You have loaded library /home/softwares/hadoop-2.7.3/lib/native/libhadoop.so 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 util.NativeCodeLoader: 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
*:"$1":*)       1
-f      1
-gn`"   1
-gt     1
-r      1
-ru`    1
-u`     1
-un`"   2
-x      1
-z      1
.       2
/etc/bashrc     1
/etc/profile    1
/etc/profile.d/ 1
/etc/profile.d/*.sh     1
/usr/bin/id     1
/usr/local/sbin 2
/usr/sbin       2
/usr/share/doc/setup-*/uidgid   1
002     1
022     1
199     1
200     1
2>/dev/null`    1
;       3
;;      1
=       4
>/dev/null      1
By      1
Current 1
EUID=`id        1
Functions       1
HISTCONTROL     1
HISTCONTROL=ignoreboth  1
HISTCONTROL=ignoredups  1
HISTSIZE        1
HISTSIZE=1000   1
HOSTNAME        1
HOSTNAME=`/usr/bin/hostname     1
It's    2
JAVA_HOME=/home/softwares/jdk1.8.0_111  1
LOGNAME 1
LOGNAME=$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
custom.sh       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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