当前位置: 澳门新濠3559 > 操作系统 > 正文

如果没有显示信息表示还没有安装jdk版本,当然

时间:2019-11-09 19:36来源:操作系统
真好最近比较闲,打算在linux 的CentOS 7Minimal版本试着搭建hadoop环境学习学习,当然第一步就是在CentOS 7 Minimal安装JDK1.8环境。其实老早就打算了解一下hadoop了,奈何太懒一拖再拖!梭哈梭

真好最近比较闲,打算在linux 的CentOS 7 Minimal版本试着搭建hadoop环境学习学习,当然第一步就是在CentOS 7 Minimal 安装JDK 1.8环境。其实老早就打算了解一下hadoop了,奈何太懒一拖再拖!梭哈梭哈,干干干!

查看java仓库版本

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。

在linux CentOS 7 Minimal 安装JDK 1.8很简单,因为是CentOS精简版,并没有安装默认JDK,几步就搞定。首先要在java官网: 下载对应linux系统的JDK版本。

yum list |grep java

如果没有显示信息表示还没有安装jdk版本,当然第一步就是在CentOS 7 Minimal。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。

图片 1

查看已安装java jdk版本

好了,言归真传。

rpm -qa | grep java

在搭建Hadoop环境中你要知道的一些事儿:

如果没有显示信息表示还没有安装jdk版本

1.hadoop运行于Linux系统之上,你要安装Linux操作系统

 如果显示如下信息:

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

rpm -qa|grep jdk
java-1.8.0-openjdk-headless-1.8.0.65-3.b17.el7.x86_64 
 java-1.7.0-openjdk-1.7.0.91-2.6.2.3.el7.x86_64 
 java-1.7.0-openjdk-headless-1.7.0.91-2.6.2.3.el7.x86_64 
 java-1.8.0-openjdk-1.8.0.65-3.b17.el7.x86_64

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

卸载命令

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

yum -y remove java-1.8.0-openjdk-headless-1.8.0.65-3.b17.el7.x86_64

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

卸载完成之后Java命令不被识别

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:

java -version

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

bash: java: command not found…

2.CentOS——下载的CentOS7的iso镜像,加载到VirtualBox中,安装运行

开始安装

3.secureCRT——可以SSH远程访问linux的软件

去官网下载jdk:

4.WinSCP——实现windows和Linux的通信

解压到安装目录

5.JDK for linux——Oracle官网上下载,解压缩之后配置一下即可

 当前版本为jdk1.8.172

6.hadoop2.7.1——可在Apache官网上下载

jdk-8u172-linux-x64.gz

好了,下面分三个步骤来讲解

建立安装目录

Linux环境准备

cd home; 
 adduser hadoop; 
 passwd hadoop; 
 whereis sudoers 
 ls -l /etc/sudoers

 配置IP

添加可写权限

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

chmod -v u+w /etc/sudoers

图片 2

然后在编辑/etc/sudoers文件

图片 3

vi /etc/sudoers

图片 4

添加如下hadoop代码:

图片 5

Allow root to run any commands anywhere
root ALL=(ALL) ALL 
 hadoop ALL=(ALL) ALL #this is new add user 
 //————————— 
ls -l /etc/sudoers

配置主机名字

去掉可写权限

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

chmod -v u-w /etc/sudoers 
 sudo -u hadoop 
 sudo cat /etc/passwd
[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 

现在hadoop用户是有sudo权限的用户了。

永久关闭防火墙

tar -zxvf jdk-8u172-linux-x64.gz -C /home/hadoop/java/

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)

更改目录名称

systemctl stop firewalld.service    #停止firewall
systemctl disable firewalld.service #禁止firewall开机启动

mv jdk1.8.0_172/ jdk1.8/

关闭SeLinux防护系统

进入jdk文件夹,获取目录绝对路径

改为disabled 。reboot重启机器,使配置生效

pwd

[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  
[root@hadoop-localhost jdk1.8]# pwd 
 /home/hadoop/java/jdk1.8

集群SSH免密码登录

(我的绝对路径是:/home/hadoop/java/jdk1.8)

首先设置ssh密钥

jdk的配置

ssh-keygen -t rsa 

使用vim命令打开系统的环境变量配置文件:

拷贝ssh密钥到三台机器

vi /etc/profile

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

在profile文件最后加入:

这样如果hadoop01的机器想要登录hadoop02,直接输入ssh hadoop02

//## JAVA 
 export JAVA_HOME=/home/hadoop/java/jdk1.8 
 //jdk的绝对路径(我的是:/home/hadoop/java/jdk1.8)
export PATH=PATH: 
PATH:
JAVA_HOME/bin
<pre name="code" class="plain">ssh hadoop02 

jdk配置验证

配置JDK

先启用配置文件

这里在/home忠诚创建三个文件夹中

source /etc/profile
[root@hadoop-localhost jdk1.8]# source /etc/profile
java -version
[root@hadoop-localhost jdk1.8]# java -version 
 java version “1.8.0_172” 
Java(TM) SE Runtime Environment (build 1.8.0_172-b11) 
 Java HotSpot(TM) 64-Bit Server VM (build 25.172-b11, mixed mode)

tools——存放工具包

总结

softwares——存放软件

以上所述是小编给大家介绍的centos7.0安装离线JDK1.8方法,希望对大家有所帮助,如果大家有任何疑问请给我留言,小编会及时回复大家的。在此也非常感谢大家对脚本之家网站的支持!

data——存放数据

您可能感兴趣的文章:

  • centOS7安装jdk1.8的方法
  • centos通过yum安装JDK1.8的教程详解
  • CentOS 7中 Minimal 安装JDK 1.8的教程
  • Linux下安装jdk1.8并配置环境变量的教程
  • RHEL6.5下JDK1.8安装教程
  • centos7.2.1511安装jdk1.8.0_151及mysql5.6.38的方法
  • yum安装openJDK1.8后无法卸载的快速解决方法
  • Ubuntu 15下安装JDK1.8教程
  • Java开发环境jdk 1.8安装配置方法(Win7 64位系统/windows server 2008)
  • JDK 1.8 安装配置教程(win7 64bit )

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图片 6

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)图片 7

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) 图片 8

 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 图片 9图片 10

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 图片 11

查看最后的词频统计结果

浏览器中查看hdfs的文件系统图片 12

[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。

您可能感兴趣的文章:

  • Linux中Centos7搭建Hadoop服务步骤
  • Hadoop2.X/YARN环境搭建--CentOS7.0 JDK配置
  • Hadoop2.X/YARN环境搭建--CentOS7.0系统配置
  • 在CentOS中搭建Hadoop的详细步骤

编辑:操作系统 本文来源:如果没有显示信息表示还没有安装jdk版本,当然

关键词: