hadoop用户 ubuntu hadoop用户组

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mob64ca141a683a 2023-09-01 10:31:56

文章标签 hadoop用户 ubuntu 配置公钥 hadoop bash mapreduce 文章分类 Hadoop 大数据

4.安装hadoop

4.1 创建hadoop用户组和hadoop用户

创建hadoop用户组和hadoop用户需要在三台机器上分别操作,这里以node1节点配置过程为例

#1.创建用户组hadoop
[root@node1 ~]# groupadd hadoop
#2.创建用户hadoop并添加到hadoop用户组中
[root@node1 ~]# useradd -g hadoop hadoop
#3.使用id命令查看hadoop用户组和hadoop用户创建是否成功
[root@node1 ~]# id hadoop
#用户uid          用户组id gid      用户组名
uid=1000(hadoop) gid=1000(hadoop) groups=1000(hadoop)
#设置hadoop用户密码为hadoop
[root@node1 ~]# passwd hadoop
Changing password for user hadoop.
New password: #输入hadoop后回车
BAD PASSWORD: The password is shorter than 8 characters
Retype new password: #再次输入hadoop后回车
passwd: all authentication tokens updated successfully.
[root@node1 ~]# chown -R hadoop:hadoop /home/hadoop/
[root@node1 ~]# chmod -R 755 /home/hadoop/
#把root用户的环境变量文件复制并覆盖hadoop用户下的.bash_profile
[root@node1 ~]# cp .bash_profile /home/hadoop/

重要的话必须说三次，三次，三次，再三次，看看下面的三行红色的字，不做，后面集群启动不了，让你后悔一万年，不懂照着做，啥都不要想,一个字就是干，一路操作猛如虎!

请参考 3.2生成公钥和私钥，3.4 验证免密码登录配置，3.5 添加本地认证公钥到认证文件中,在hadoop用户下，对hadoop用户做免密码登录配置

[hadoop@node1 ~] su - hadoop
[hadoop@node1 ~] source.bash_profile
#使用su - hadoop切换到hadoop用户下执行如下操作
[hadoop@node1 ~]# ssh-keygen -t rsa  #<--回车
Generating public/private rsa key pair.
Enter file in which to save the key (/root/.ssh/id_rsa): #<--回车
#会在root用户的家目录下生成.ssh目录，此目录中会保存生成的公钥和私钥
Created directory '/root/.ssh'.
Enter passphrase (empty for no passphrase): #<--回车
Enter same passphrase again: 
Your identification has been saved in /root/.ssh/id_rsa.
Your public key has been saved in /root/.ssh/id_rsa.pub.
The key fingerprint is:
SHA256:gpDw08iG9Tq+sGZ48TXirWTY17ajXhIea3drjy+pU3g root@node1
The key's randomart image is:
+---[RSA 2048]----+
|. .              |
| * =             |
|. O o            |
| . + .           |
|  o . + S.       |
| ..+..o*. E      |
|o o+++*.=o..     |
|.=.+oo.=oo+o     |
|+.. .oo.o=o+o    |
+----[SHA256]-----+
You have new mail in /var/spool/mail/root
[hadoop@node1 ~]# 

#修改.ssh目录权限
[hadoop@node1 ~]$ chmod -R 755 .ssh/
[hadoop@node1 ~]$ cd .ssh/
[hadoop@node1 .ssh]$ chmod 644 *
[hadoop@node1 .ssh]$ chmod 600 id_rsa
[hadoop@node1 .ssh]$ chmod 600 id_rsa.pub 
[hadoop@node1 .ssh]$

4.2 配置hadoop

在一台机器上配置好后复制到其他机器上即可,这样保证三台机器的hadoop配置是一致的.

1.上传hadoop安装包,进行解压

#1.创建hadoop安装目录
[root@node1 ~]# mkdir -p /opt/bigdata
#2.解压hadoop-3.1.2.tar.gz
[root@node1 ~]# tar -xzvf hadoop-3.1.2.tar.gz -C /opt/bigdata/

2.配置hadoop环境变量

1.配置环境变量

[root@node1 ~]# vi .bash_profile 

# .bash_profile

# Get the aliases and functions
if [ -f ~/.bashrc ]; then
        . ~/.bashrc
fi

# User specific environment and startup programs
JAVA_HOME=/usr/java/jdk1.8.0_211-amd64
HADOOP_HOME=/opt/bigdata/hadoop-3.1.2

PATH=$PATH:$HOME/bin:$JAVA_HOME/bin:$HADOOP_HOME/bin:$HADOOP_HOME/sbin

export JAVA_HOME
export HADOOP_HOME
export PATH
~                                                                                                                                                                                                       
:wq!

2.验证环境变量

#1.使环境变量生效
[root@node1 ~]# source .bash_profile 
#2.显示hadoop的版本信息
[root@node1 ~]# hadoop version
#3.显示出hadoop版本信息表示安装和环境变量成功.
Hadoop 3.1.2
Source code repository https://github.com/apache/hadoop.git -r 1019dde65bcf12e05ef48ac71e84550d589e5d9a
Compiled by sunilg on 2019-01-29T01:39Z
Compiled with protoc 2.5.0
From source with checksum 64b8bdd4ca6e77cce75a93eb09ab2a9
This command was run using /opt/bigdata/hadoop-3.1.2/share/hadoop/common/hadoop-common-3.1.2.jar
[root@node1 ~]#

hadoop用户下也需要按照root用户配置环境变量的方式操作一下

3.配置hadoop-env.sh

这个文件只需要配置JAVA_HOME的值即可,在文件中找到export JAVA_HOME字眼的位置，删除最前面的#

export JAVA_HOME=/usr/java/jdk1.8.0_211-amd64
[root@node1 ~]# cd /opt/bigdata/hadoop-3.1.2/etc/hadoop/
You have new mail in /var/spool/mail/root
[root@node1 hadoop]# pwd
/opt/bigdata/hadoop-3.1.2/etc/hadoop
[root@node1 hadoop]# vi hadoop-env.sh

4.配置core-site.xml

切换到cd /opt/bigdata/hadoop-3.1.2/etc/hadoop/目录下

[root@node1 ~]# cd /opt/bigdata/hadoop-3.1.2/etc/hadoop/
<configuration>
 <!-- 指定hdfs的namenode主机的hostname -->
  <property>
      <name>fs.defaultFS</name>
      <value>hdfs://node1:9000</value>
  </property>
<!-- io操作流的配置 -->
  <property>
      <name>io.file.buffer.size</name>
      <value>131072</value>
  </property>   
 <!--hadoop集群临时数据存储目录-->
  <property>
       <name>hadoop.tmp.dir</name>
       <value>/opt/bigdata/hadoop-3.1.2/tmpdata</value>
  </property>
</configuration>

5.配置hdfs-site.xml

配置/opt/bigdata/hadoop-3.1.2/etc/hadoop/目录下的hdfs-site.xml

<configuration>
    <!--namenode元数据存储目录-->
    <property>
      <name>dfs.namenode.name.dir</name>
      <value>/opt/bigdata/hadoop-3.1.2/hadoop/hdfs/name/</value>
    </property>
    <!--指定block块的的大小-->
    <property>
      <name>dfs.blocksize</name>
      <value>268435456</value>
    </property>
    <!-- -->
    <property>
      <name>dfs.namenode.handler.count</name>
      <value>100</value>
    </property>

    <!--工作节点的数据块存储目录 -->
    <property>
      <name>dfs.datanode.data.dir</name>
      <value>/opt/bigdata/hadoop-3.1.2/hadoop/hdfs/data/</value>
    </property>
    <!--block的副本数-->
    <property>
        <name>dfs.replication</name>
        <value>3</value>
    </property>
</configuration>

6.配置mapred-site.xml

配置/opt/bigdata/hadoop-3.1.2/etc/hadoop/目录下的mapred-site.xml

<!--指定运行mapreduce的环境是yarn -->
<configuration>
   <property>
       <name>mapreduce.framework.name</name>
       <value>yarn</value>
   </property>
  <property>
    <name>yarn.app.mapreduce.am.env</name>
    <value>HADOOP_MAPRED_HOME=${HADOOP_HOME}</value>
  </property>
  <property>
    <name>mapreduce.map.env</name>
    <value>HADOOP_MAPRED_HOME=${HADOOP_HOME}</value>
  </property>
  <property>
    <name>mapreduce.reduce.env</name>
    <value>HADOOP_MAPRED_HOME=${HADOOP_HOME}</value>
  </property>
    <!--以下配置复制到配置文件中，大家需要修改value中的，（逗号）为英文输入法的逗号-->
    <property>
        <name>mapreduce.application.classpath</name>
  <value>$HADOOP_MAPRED_HOME/share/hadoop/mapreduce/*,                   $HADOOP_MAPRED_HOME/share/hadoop/mapreduce/lib/*</value>
    </property>
</configuration>

7.配置yarn-site.xml

配置/opt/bigdata/hadoop-3.1.2/etc/hadoop/目录下的yarn-site.xml

<configuration>
<!--指定resourcemanager的位置-->
  <property>
    <name>yarn.resourcemanager.hostname</name>
    <value>node1</value>
  </property>
  <property>
    <name>yarn.nodemanager.aux-services</name>
    <value>mapreduce_shuffle</value>
  </property>
  <property>
    <name>yarn.resourcemanager.address</name>
    <value>node1:18040</value>
  </property>
  <property>                
    <name>yarn.resourcemanager.scheduler.address</name>
    <value>node1:18030</value>
  </property>
  <property>
     <name>yarn.resourcemanager.resource-tracker.address</name>
     <value>node1:18025</value>
  </property>
  <property>                
     <name>yarn.resourcemanager.admin.address</name>
     <value>node1:18141</value>
  </property>
  <property>                
      <name>yarn.resourcemanager.webapp.address</name>
      <value>node1:18088</value>
  </property>
</configuration>

8.编辑works

此文件用于配置集群有多少个数据节点,我们把node2，node3作为数据节点,node1作为集群管理节点.

配置/opt/bigdata/hadoop-3.1.2/etc/hadoop/目录下的works

[root@node1 hadoop]# vi workers 
#将localhost这一行删除掉
node2
node3
~

4.3 远程复制hadoop到集群机器

#1.进入到root用户家目录下
[root@node1 hadoop]# cd ~
#2.使用scp远程拷贝命令将root用户的环境变量配置文件复制到node2
[root@node1 ~]# scp .bash_profile root@node2:~
.bash_profile                                                                   100%  338   566.5KB/s   00:00    
#3.使用scp远程拷贝命令将root用户的环境变量配置文件复制到node3
[root@node1 ~]# scp .bash_profile root@node3:~
.bash_profile                                                                   100%  338   212.6KB/s   00:00    
[root@node1 ~]# 

#4.进入到hadoop的share目录下
[root@node1 ~]# cd /opt/bigdata/hadoop-3.1.2/share/
You have new mail in /var/spool/mail/root
[root@node1 share]# ll
total 0
drwxr-xr-x 3 1001 1002 20 Jan 29 12:05 doc
drwxr-xr-x 8 1001 1002 88 Jan 29 11:36 hadoop
#5.删除doc目录,这个目录存放的是用户手册，比较大，等会儿下面进行远程复制的时候时间比较长，删除后节约复制时间
[root@node1 share]# rm -rf doc/
[root@node1 share]# cd ~
You have new mail in /var/spool/mail/root
[root@node1 ~]# scp -r /opt root@node2:/
[root@node1 ~]# scp -r /opt root@node3:/

4.4 使集群所有机器环境变量生效

在node2，node3的root用户家目录下使环境变量生效

node2节点如下操作:

[root@node2 hadoop-3.1.2]# cd ~
[root@node2 ~]# source .bash_profile 
[root@node2 ~]# hadoop version
Hadoop 3.1.2
Source code repository https://github.com/apache/hadoop.git -r 1019dde65bcf12e05ef48ac71e84550d589e5d9a
Compiled by sunilg on 2019-01-29T01:39Z
Compiled with protoc 2.5.0
From source with checksum 64b8bdd4ca6e77cce75a93eb09ab2a9
This command was run using /opt/bigdata/hadoop-3.1.2/share/hadoop/common/hadoop-common-3.1.2.jar
[root@node2 ~]#

node3节点如下操作:

[root@node3 bin]# cd ~
[root@node3 ~]# source .bash_profile 
[root@node3 ~]# hadoop version
Hadoop 3.1.2
Source code repository https://github.com/apache/hadoop.git -r 1019dde65bcf12e05ef48ac71e84550d589e5d9a
Compiled by sunilg on 2019-01-29T01:39Z
Compiled with protoc 2.5.0
From source with checksum 64b8bdd4ca6e77cce75a93eb09ab2a9
This command was run using /opt/bigdata/hadoop-3.1.2/share/hadoop/common/hadoop-common-3.1.2.jar
[root@node3 ~]#

5.修改hadoop安装目录的权限

node2，node3也需要进行如下操作

#1.修改目录所属用户和组为hadoop:hadoop
[root@node1 ~]# chown -R hadoop:hadoop /opt/
You have new mail in /var/spool/mail/root
You have new mail in /var/spool/mail/root
#2.修改目录所属用户和组的权限值为755
[root@node1 ~]# chmod -R 755  /opt/
[root@node1 ~]# chmod -R g+w /opt/
[root@node1 ~]# chmod -R o+w /opt/
[root@node1 ~]#

6.格式化hadoop

#切换
[root@node1 ~]# su - hadoop
[hadoop@node1 hadoop]$  hdfs namenode -format
2019-06-30 16:11:35,914 INFO namenode.NameNode: STARTUP_MSG: 
/************************************************************
STARTUP_MSG: Starting NameNode
STARTUP_MSG:   host = node1/192.168.200.11
STARTUP_MSG:   args = [-format]
STARTUP_MSG:   version = 3.1.2
#此处省略部分日志
2019-06-30 16:11:36,636 INFO namenode.NameNode: SHUTDOWN_MSG: 
/************************************************************
SHUTDOWN_MSG: Shutting down NameNode at node1/192.168.200.11
************************************************************/
[hadoop@node1 hadoop]$

hadoop用户 ubuntu hadoop用户组_bash

7.启动集群

[hadoop@node1 ~]$ start-all.sh 
WARNING: Attempting to start all Apache Hadoop daemons as hadoop in 10 seconds.
WARNING: This is not a recommended production deployment configuration.
WARNING: Use CTRL-C to abort.
Starting namenodes on [node1]
Starting datanodes
Starting secondary namenodes [node1]
Starting resourcemanager
Starting nodemanagers
#使用jps显示java进程
[hadoop@node1 ~]$ jps
40852 ResourceManager 
40294 NameNode
40615 SecondaryNameNode
41164 Jps
[hadoop@node1 ~]$

在浏览器地址栏中输入:http://192.168.200.11:9870查看namenode的web界面.

hadoop用户 ubuntu hadoop用户组_hadoop用户 ubuntu_02

8.运行mapreduce程序

mapreduce程序(行话程为词频统计程序(中文名),英文名：wordcount)，就是统计一个文件中每一个单词出现的次数，也是我们学习大数据技术最基础，最简单的程序，入门必须要会要懂的第一个程序，其地位和java，php，c#，javascript等编程语言的第一个入门程序HelloWorld(在控制台打印“hello world！”等字样)程序一样，尤为重要,不同的是它们是单机应用程序，我们接下来要运行的程序(wordcount)是一个分布式运行的程序，是在一个大数据集群中运行的程序。wordcount程序能够正常的运行成功，输入结果，意味着我们的大数据环境正确的安装和配置成功。好，简单的先介绍到这里，接下来让我们爽一把吧。

#1.使用hdfs dfs -ls /  命令浏览hdfs文件系统，集群刚开始搭建好，由于没有任何目录所以什么都不显示.
[hadoop@node1 ~]$ hdfs dfs -ls /
#2.创建测试目录
[hadoop@node1 ~]$ hdfs dfs -mkdir /test 
#3.在此使用hdfs dfs -ls 发现我们刚才创建的test目录
[hadoop@node1 ~]$ hdfs dfs -ls /
Found 1 items
drwxr-xr-x   - hadoop supergroup          0 2019-06-30 17:23 /test
#4.使用touch命令在linux本地目录创建一个words文件
[hadoop@node1 ~]$ touch words
#5.文件中输入如下内容
[hadoop@node1 ~]$ vi words
i love you
are you ok

#6.将创建的本地words文件上传到hdfs的test目录下
[hadoop@node1 ~]$ hdfs dfs -put words /test
#7.查看上传的文件是否成功
[hadoop@node1 ~]$ hdfs dfs -ls -r /test
Found 1 items
-rw-r--r--   3 hadoop supergroup         23 2019-06-30 17:28 /test/words
#/test/words 是hdfs上的文件存储路径 /test/output是mapreduce程序的输出路径，这个输出路径是不能已经存在的路径，mapreduce程序运行的过程中会自动创建输出路径，数据路径存在的话会报错，这里需要同学注意下.
[hadoop@node1 ~]$ hadoop jar /opt/bigdata/hadoop-3.1.2/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.2.jar  wordcount /test/words /test/output
2019-06-30 17:32:23,685 INFO client.RMProxy: Connecting to ResourceManager at node1/192.168.200.11:18040
2019-06-30 17:32:24,060 INFO mapreduce.JobResourceUploader: Disabling Erasure Coding for path: /tmp/hadoop-yarn/staging/hadoop/.staging/job_1561886252942_0001
2019-06-30 17:32:24,215 INFO input.FileInputFormat: Total input files to process : 1
2019-06-30 17:32:24,291 INFO mapreduce.JobSubmitter: number of splits:1
2019-06-30 17:32:24,394 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1561886252942_0001
2019-06-30 17:32:24,395 INFO mapreduce.JobSubmitter: Executing with tokens: []
2019-06-30 17:32:24,592 INFO conf.Configuration: resource-types.xml not found
2019-06-30 17:32:24,593 INFO resource.ResourceUtils: Unable to find 'resource-types.xml'.
2019-06-30 17:32:24,806 INFO impl.YarnClientImpl: Submitted application application_1561886252942_0001
2019-06-30 17:32:24,861 INFO mapreduce.Job: The url to track the job: http://node1:18088/proxy/application_1561886252942_0001/
2019-06-30 17:32:24,862 INFO mapreduce.Job: Running job: job_1561886252942_0001
2019-06-30 17:32:33,025 INFO mapreduce.Job: Job job_1561886252942_0001 running in uber mode : false
2019-06-30 17:32:33,030 INFO mapreduce.Job:  map 0% reduce 0%
2019-06-30 17:32:39,174 INFO mapreduce.Job:  map 100% reduce 0%
2019-06-30 17:32:43,229 INFO mapreduce.Job:  map 100% reduce 100%
2019-06-30 17:32:43,266 INFO mapreduce.Job: Job job_1561886252942_0001 completed successfully
2019-06-30 17:32:43,369 INFO mapreduce.Job: Counters: 53
    File System Counters
        FILE: Number of bytes read=54
        FILE: Number of bytes written=432335
        FILE: Number of read operations=0
        FILE: Number of large read operations=0
        FILE: Number of write operations=0
        HDFS: Number of bytes read=116
        HDFS: Number of bytes written=28
        HDFS: Number of read operations=8
        HDFS: Number of large read operations=0
        HDFS: Number of write operations=2
    Job Counters 
        Launched map tasks=1
        Launched reduce tasks=1
        Data-local map tasks=1
        Total time spent by all maps in occupied slots (ms)=3346
        Total time spent by all reduces in occupied slots (ms)=2290
        Total time spent by all map tasks (ms)=3346
        Total time spent by all reduce tasks (ms)=2290
        Total vcore-milliseconds taken by all map tasks=3346
        Total vcore-milliseconds taken by all reduce tasks=2290
        Total megabyte-milliseconds taken by all map tasks=3426304
        Total megabyte-milliseconds taken by all reduce tasks=2344960
    Map-Reduce Framework
        Map input records=2
        Map output records=6
        Map output bytes=46
        Map output materialized bytes=54
        Input split bytes=93
        Combine input records=6
        Combine output records=5
        Reduce input groups=5
        Reduce shuffle bytes=54
        Reduce input records=5
        Reduce output records=5
        Spilled Records=10
        Shuffled Maps =1
        Failed Shuffles=0
        Merged Map outputs=1
        GC time elapsed (ms)=472
        CPU time spent (ms)=1380
        Physical memory (bytes) snapshot=492576768
        Virtual memory (bytes) snapshot=5577179136
        Total committed heap usage (bytes)=407371776
        Peak Map Physical memory (bytes)=294256640
        Peak Map Virtual memory (bytes)=2788634624
        Peak Reduce Physical memory (bytes)=198320128
        Peak Reduce Virtual memory (bytes)=2788544512
    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=23
    File Output Format Counters 
        Bytes Written=28
[hadoop@node1 ~]$ hdfs dfs -ls -r /test/output
Found 2 items
-rw-r--r--   3 hadoop supergroup         28 2019-06-30 17:32 /test/output/part-r-00000
-rw-r--r--   3 hadoop supergroup          0 2019-06-30 17:32 /test/output/_SUCCESS
[hadoop@node1 ~]$ hdfs dfs -text /test/output/part-r-00000
are 1
i   1
love    1
ok  1
you 2
[hadoop@node1 ~]$

9.停止集群

[hadoop@node1 ~]$ stop-all.sh

至此三节点的hadoop集群环境搭建完成，谢谢大家能够有耐心的学习完!

hadoop用户 ubuntu hadoop用户组_配置公钥_03

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