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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
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.outjps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
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} 1这就代表hadoop集群正确
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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