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
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脚本之家。
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