基于CentOS的Hadoop分布式环境的搭建开发

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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 
$UID  1 
&&   1 
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-un`"  2 
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/etc/bashrc   1 
/etc/profile  1 
/etc/profiled/ 1 
/etc/profiled/*sh   1 
/usr/bin/id   1 
/usr/local/sbin 2 
/usr/sbin    2 
/usr/share/doc/setup-*/uidgid  1 
002   1 
022   1 
199   1 
200   1 
2>/dev/null`  1 
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>/dev/null   1 
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EUID=`id    1 
Functions    1 
HISTCONTROL   1 
HISTCONTROL=ignoreboth 1 
HISTCONTROL=ignoredups 1 
HISTSIZE    1 
HISTSIZE=1000  1 
HOSTNAME    1 
HOSTNAME=`/usr/bin/hostname   1 
It's  2 
JAVA_HOME=/home/softwares/jdk0_111 1 
LOGNAME 1 
LOGNAME=$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 
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UID=`id 1 
USER  1 
USER="`id    1 
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[    9 
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a    2 
after  2 
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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。

centos搭建hadoop环境,hadoop分布式环境搭建,hadoop分布式搭建

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