基于CentOS的Hadoop分布式环境如何搭建
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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firewallsystemctl 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 192.168.56.101ssh-copy-id 192.168.56.102ssh-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
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
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:/homehadoop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
fsdefaultfs hdfs://101:8020 hadooptmpdir /home/softwares/hadoop-3/data/tmp fstrashinterval 10080 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
dfsreplication 3 dfspermissionsenabled false dfsnamenodehttp-address 101:50070 dfsnamenodesecondaryhttp-address 103:50090 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
mapreduceframeworkname yarn mapreducejobhistoryaddress 101:10020 mapreducejobhistorywebappaddress 101:19888 mapreducejobubertaskenable true 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
yarnnodemanageraux-services mapreduce_shuffle yarnresourcemanagerhostname 102 yarnweb-proxyaddress 102:8888 yarnlog-aggregation-enable true yarnlog-aggregationretain-seconds 604800 yarnnodemanagerresourcememory-mb 8192 yarnnodemanagerresourcecpu-vcores 8 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', 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-hadoopoutjps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', 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', 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 感谢各位的阅读，以上就是"基于CentOS的Hadoop分布式环境如何搭建"的内容了，经过本文的学习后，相信大家对基于CentOS的Hadoop分布式环境如何搭建这一问题有了更深刻的体会，具体使用情况还需要大家实践验证。这里是，小编将为大家推送更多相关知识点的文章，欢迎关注！
