1、准备

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1.1、在vmware上安装centos7的虚拟机

1.2、系统配置

配置网络

# vi /etc/sysconfig/network-scripts/ifcfg-ens33

BOOTPROTO=static

ONBOOT=yes

IPADDR=192.168.120.131

GATEWAY=192.168.120.2

NETMASK=255.255.255.0

DNS1=8.8.8.8

DNS2=4.4.4.4

1.3、配置主机名

# hostnamectl set-hostname master1

# hostname master1

1.4、指定时区（如果时区不是上海）

# ll /etc/localtime

lrwxrwxrwx. 1 root root 35 6月   4 19:25 /etc/localtime -> ../usr/share/zoneinfo/Asia/Shanghai

如果时区不对的话需要修改时区，方法：

# ln -sf /usr/share/zoneinfo/Asia/Shanghai /etc/localtime

1.5、上传包

hadoop-2.9.1.tar

jdk-8u171-linux-x64.tar

2、开始搭建环境

2.1、创建用户和组

[root@master1 ~]# groupadd hadoop

[root@master1 ~]# useradd -g hadoop hadoop

[root@master1 ~]# passwd hadoop

2.2、解压包

切换用户

[root@master1 ~]# su hadoop

创建存放包的目录

[hadoop@master1 root]$ cd

[hadoop@master1 ~]$ mkdir src

[hadoop@master1 ~]$ mv *.tar src

解压包

[hadoop@master1 ~]$ cd src

[hadoop@master1 src]$ tar -xf jdk-8u171-linux-x64.tar -C ../

[hadoop@master1 src]$ tar xf hadoop-2.9.1.tar -C ../

[hadoop@master1 src]$ cd

[hadoop@master1 ~]$ mv jdk1.8.0_171 jdk

[hadoop@master1 ~]$ mv hadoop-2.9.1 hadoop

2.3、配置环境变量

[hadoop@master1 ~]$ vi .bashrc

export JAVA_HOME=/home/hadoop/jdk

export JRE_HOME=/$JAVA_HOME/jre

export CLASSPATH=.:$JAVA_HOME/lib

export PATH=$PATH:$JAVA_HOME/bin

export HADOOP_HOME=/home/hadoop/hadoop

export HADOOP_INSTALL=$HADOOP_HOME

export HADOOP_MAPRED_HOME=$HADOOP_HOME

export HADOOP_COMMON_HOME=$HADOOP_HOME

export HADOOP_HDFS_HOME=$HADOOP_HOME

export YARN_HOME=$HADOOP_HOME

export HADOOP_COMMON_LIB_NATIVE_DIR=$HADOOP_HOME/lib/native

export PATH=$PATH:$HADOOP_HOME/sbin:$HADOOP_HOME/bin

使配置文件生效

[hadoop@master1 ~]$ source .bashrc

验证

[hadoop@master1 ~]$ java -version

java version "1.8.0_171"

Java(TM) SE Runtime Environment (build 1.8.0_171-b11)

Java HotSpot(TM) 64-Bit Server VM (build 25.171-b11, mixed mode)

[hadoop@master1 ~]$ hadoop version

Hadoop 2.9.1

Subversion https://github.com/apache/hadoop.git -r e30710aea4e6e55e69372929106cf119af06fd0e

Compiled by root on 2018-04-16T09:33Z

Compiled with protoc 2.5.0

From source with checksum 7d6d2b655115c6cc336d662cc2b919bd

This command was run using /home/hadoop/hadoop/share/hadoop/common/hadoop-common-2.9.1.jar

2.4、修改hadoop配置文件

[hadoop@master1 ~]$ cd hadoop/etc/hadoop/

[hadoop@master1 hadoop]$ vi hadoop-env.sh

export JAVA_HOME=/home/hadoop/jdk

[hadoop@master1 hadoop]$ vi core-site.xml

fs.defaultFS

hdfs://192.168.120.131:9000

hadoop.tmp.dir

/data/hadoop/hadoop_tmp_dir

说明：

fs.defaultFS:这个属性用来指定namenode的hdfs协议的文件系统通信地址，可以指定一个主机+端口，也可以指定一个namenode服务（这个服务内部可以有多台namenode实现ha的namenode服务）

hadoop.tmp.dir:hadoop集群在工作的时候存储的一些临时文件的目录

[hadoop@master1 hadoop]$ vi hdfs-site.xml

dfs.replication

1

说明：

dfs.replication:hdfs的副本数设置。也就是上传一个文件，其分割的block块后，每个block的冗余副本个数，默认配置是3。

下面的参数以配置就会出现datanode无法启动的问题，所以不做配置，尚未搞明白怎么出现的。

dfs.namenode.name.dir:namenode数据的存放目录。也就是namenode元数据存放的目录，记录了hdfs系统中文件的元数据。

dfs.datanode.data.dir:datanode数据的存放目录。也就是block块的存放目录。

下面贴出异常信息

[hadoop@master1 logs]$ pwd

/home/hadoop/hadoop/logs

[hadoop@master1 logs]$ tail -f hadoop-hadoop-datanode-master1.log

2018-06-12 22:30:14,749 WARN org.apache.hadoop.hdfs.server.common.Storage: Failed to add storage directory [DISK]file:/data/hadoop/hdfs/dn/

java.io.IOException: Incompatible clusterIDs in /data/hadoop/hdfs/dn: namenode clusterID = CID-5bbc555b-4622-4781-9a7f-c2e5131e4869; datanode clusterID = CID-29ec402d-95f8-4148-8d18-f7e4b965be4f

at org.apache.hadoop.hdfs.server.datanode.DataStorage.doTransition(DataStorage.java:760)

2018-06-12 22:30:14,752 ERROR org.apache.hadoop.hdfs.server.datanode.DataNode: Initialization failed for Block pool (Datanode Uuid f39576ae-b7af-44aa-841a-48ba03b956f4) service to master1/192.168.120.131:9000. Exiting.

java.io.IOException: All specified directories have failed to load.

at org.apache.hadoop.hdfs.server.datanode.DataStorage.recoverTransitionRead(DataStorage.java:557)

2018-06-12 22:30:14,753 WARN org.apache.hadoop.hdfs.server.datanode.DataNode: Ending block pool service for: Block pool (Datanode Uuid f39576ae-b7af-44aa-841a-48ba03b956f4) service to master1/192.168.120.131:9000

2018-06-12 22:30:14,854 INFO org.apache.hadoop.hdfs.server.datanode.DataNode: Removed Block pool (Datanode Uuid f39576ae-b7af-44aa-841a-48ba03b956f4)

2018-06-12 22:30:16,855 WARN org.apache.hadoop.hdfs.server.datanode.DataNode: Exiting Datanode

2018-06-12 22:30:16,916 INFO org.apache.hadoop.hdfs.server.datanode.DataNode: SHUTDOWN_MSG:

/************************************************************

SHUTDOWN_MSG: Shutting down DataNode at master1/192.168.120.131

[hadoop@master1 hadoop]$ cp mapred-site.xml.template mapred-site.xml

[hadoop@master1 hadoop]$ vi mapred-site.xml

mapreduce.framework.name

yarn

说明:

mapreduce.framework.name:指定mr框架为yarn方式，Hadoop二代MP也基于Yarn来运行。

[hadoop@master1 hadoop]$ vi yarn-site.xml

yarn.resourcemanager.hostname

192.168.120.131

yarn.nodemanager.aux-services

mapreduce_shuffle

说明：

yarn.resourcemanager.hostname:yarn总管理器的IPC通讯地址，可以是IP也可以是主机名。

yarn.nodemanager.aux-service:集群为MapReduce程序提供的shuffle服务

2.5、创建目录并赋予权限

[hadoop@master1 hadoop]$ exit

[root@master1 ~]# mkdir -p /data/hadoop/hadoop_tmp_dir

[root@master1 ~]# mkdir -p /data/hadoop/hdfs/{nn,dn}

[root@master1 ~]# chown -R hadoop:hadoop /data

3、格式化文件系统并启动服务

3.1、格式化文件系统

[root@master1 ~]# su hadoop

[hadoop@master1 ~]$ cd hadoop/bin

[hadoop@master1 bin]$ ./hdfs namenode -format

注意：

如果是集群环境，HDFS初始化只能在主节点上运行

3.2、启动HDFS

[hadoop@master1 bin]$ cd sbin

[hadoop@master1 sbin]$ ./start-dfs.sh

注意：

如果是集群环境，不管在集群中的哪个节点都可以运行

如果有个别服务启动失败，配置也没有问题的话，很有可能是创建的目录权限问题

3.3、启动YARN

[hadoop@master1 sbin]$ ./start-yarn.sh

注意：

如果是集群环境，只能在主节点中运行

查看服务状态

[hadoop@master1 sbin]$ jps

6708 NameNode

6966 SecondaryNameNode

6808 DataNode

7116 Jps

5791 ResourceManager

5903 NodeManager

3.4、浏览器查看服务状态

使用web查看HSFS运行状态

在浏览器输入

http://192.168.120.131:50070

使用web查看YARN运行状态

在浏览器输入

http://192.168.120.131:8088

4、启动ssh无密码验证

上面启动服务时还需要输入用户名登录密码，如下所示：

[hadoop@master1 sbin]$ ./start-yarn.sh

starting yarn daemons

starting resourcemanager, logging to /home/hadoop/hadoop/logs/yarn-hadoop-resourcemanager-master1.out

hadoop@localhost's password:

如果想要做到无密码启动服务的话需要配置ssh

[hadoop@master1 sbin]$ cd ~/.ssh/

[hadoop@master1 .ssh]$ ll

总用量 4

-rw-r--r--. 1 hadoop hadoop 372 6月  12 18:36 known_hosts

[hadoop@master1 .ssh]$ ssh-keygen

Generating public/private rsa key pair.

Enter file in which to save the key (/home/hadoop/.ssh/id_rsa):

Enter passphrase (empty for no passphrase):

Enter same passphrase again:

Your identification has been saved in /home/hadoop/.ssh/id_rsa.

Your public key has been saved in /home/hadoop/.ssh/id_rsa.pub.

The key fingerprint is:

SHA256:D14LpPKZbih0K+kVoTl23zGsKK1xOVlNuSugDvrkjJA hadoop@master1

The key's randomart image is:

+---[RSA 2048]----+

|                 |

|         .       |

|    .   +        |

|   o . * .       |

|  = = o S .      |

| o.=.@ * O .     |

|E.=oOoB + o      |

|oB+*oo..         |

|ooBo ..          |

+----[SHA256]-----+

一路按下enter键就行

[hadoop@master1 .ssh]$ ll

总用量 12

-rw-------. 1 hadoop hadoop 1675 6月  12 18:46 id_rsa

-rw-r--r--. 1 hadoop hadoop  396 6月  12 18:46 id_rsa.pub

-rw-r--r--. 1 hadoop hadoop  372 6月  12 18:36 known_hosts

[hadoop@master1 .ssh]$ cat id_rsa.pub >> ~/.ssh/authorized_keys

[hadoop@master1 .ssh]$ ll

总用量 16

-rw-rw-r--. 1 hadoop hadoop  396 6月  12 18:47 authorized_keys

-rw-------. 1 hadoop hadoop 1675 6月  12 18:46 id_rsa

-rw-r--r--. 1 hadoop hadoop  396 6月  12 18:46 id_rsa.pub

-rw-r--r--. 1 hadoop hadoop  372 6月  12 18:36 known_hosts

如果发现还需要输入密码才能登录，这是因为文件权限的问题，改下权限就可以

[hadoop@master1 .ssh]$ chmod 600 authorized_keys

发现可以实现无密码登录了

[hadoop@master1 .ssh]$ ssh localhost

Last login: Tue Jun 12 18:48:38 2018 from fe80::e961:7d5b:6a72:a2a9%ens33

[hadoop@master1 ~]$

 

当然无密登录的实现还可以用另一种方法实现

在执行完ssh-keygen之后

执行下面的命令

ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop@master1

5、文件系统的简单应用及遇到的一些问题

5.1、创建目录

在文件系统中创建目录

[hadoop@master1 bin]$ hdfs dfs -mkdir -p /user/hadoop

18/06/12 21:25:31 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable

列出创建的目录

[hadoop@master1 bin]$ hdfs dfs -ls /

18/06/12 21:29:55 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable

Found 1 items

drwxr-xr-x   - hadoop supergroup          0 2018-06-12 21:25 /user

   

5.2、解决警告问题

有WARN警告，但是并不影响Hadoop正常使用。

两种方式可以解决这个报警问题，方法一是重新编译源码，方法二是在日志中取消告警信息，我采用的是第二种方式。

[hadoop@master1 ]$ cd /home/hadoop/hadoop/etc/hadoop/

[hadoop@master1 hadoop]$ vi log4j.properties

添加

#native WARN

log4j.logger.org.apache.hadoop.util.NativeCodeLoader=ERROR

可以看到效果了

[hadoop@master1 hadoop]$ hdfs dfs -ls /

Found 1 items

drwxr-xr-x   - hadoop supergroup          0 2018-06-12 21:25 /user

5.3、上传文件到hdfs文件系统中

[hadoop@master1 bin]$ hdfs dfs -mkdir -p input

[hadoop@master1 hadoop]$ hdfs dfs -put /home/hadoop/hadoop/etc/hadoop input

Hadoop默认附带了丰富的例子：包括wordcoun，terasort,join,grep等，执行下面的命令查看：

[hadoop@master1 bin]$ hadoop jar /home/hadoop/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.9.1.jar

An example program must be given as the first argument.

Valid program names are:

aggregatewordcount: An Aggregate based map/reduce program that counts the words in the input files.

aggregatewordhist: An Aggregate based map/reduce program that computes the histogram of the words in the input files.

bbp: A map/reduce program that uses Bailey-Borwein-Plouffe to compute exact digits of Pi.

dbcount: An example job that count the pageview counts from a database.

distbbp: A map/reduce program that uses a BBP-type formula to compute exact bits of Pi.

grep: A map/reduce program that counts the matches of a regex in the input.

join: A job that effects a join over sorted, equally partitioned datasets

multifilewc: A job that counts words from several files.

pentomino: A map/reduce tile laying program to find solutions to pentomino problems.

pi: A map/reduce program that estimates Pi using a quasi-Monte Carlo method.

randomtextwriter: A map/reduce program that writes 10GB of random textual data per node.

randomwriter: A map/reduce program that writes 10GB of random data per node.

secondarysort: An example defining a secondary sort to the reduce.

sort: A map/reduce program that sorts the data written by the random writer.

sudoku: A sudoku solver.

teragen: Generate data for the terasort

terasort: Run the terasort

teravalidate: Checking results of terasort

wordcount: A map/reduce program that counts the words in the input files.

wordmean: A map/reduce program that counts the average length of the words in the input files.

wordmedian: A map/reduce program that counts the median length of the words in the input files.

wordstandarddeviation: A map/reduce program that counts the standard deviation of the length of the words in the input files.

伪分布式运行MapReduce作业的方式跟单机模式相同，区别在于伪分布式方式读取的是HDFS中的文件（可以将单机步骤中创建的本地input文件夹，输出结果output文件夹都删除来验证这一点）。

[hadoop@master1 sbin]$ hadoop jar /home/hadoop/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.9.1.jar grep input output 'dfs[a-z]+'

18/06/12 22:57:05 INFO client.RMProxy: Connecting to ResourceManager at /192.168.120.131:8032

18/06/12 22:57:07 INFO input.FileInputFormat: Total input files to process : 30

省略。。。

18/06/12 22:57:08 INFO mapreduce.Job: Running job: job_1528815135795_0001

18/06/12 22:57:23 INFO mapreduce.Job: Job job_1528815135795_0001 running in uber mode : false

18/06/12 22:57:23 INFO mapreduce.Job:  map 0% reduce 0%

18/06/12 22:58:02 INFO mapreduce.Job:  map 13% reduce 0%

省略。。。

18/06/12 23:00:17 INFO mapreduce.Job:  map 97% reduce 32%

18/06/12 23:00:18 INFO mapreduce.Job:  map 100% reduce 32%

18/06/12 23:00:19 INFO mapreduce.Job:  map 100% reduce 100%

18/06/12 23:00:20 INFO mapreduce.Job: Job job_1528815135795_0001 completed successfully

18/06/12 23:00:20 INFO mapreduce.Job: Counters: 50

File System Counters

FILE: Number of bytes read=46

FILE: Number of bytes written=6136681

FILE: Number of read operations=0

省略。。。

File Input Format Counters

Bytes Read=138

File Output Format Counters

Bytes Written=24

查看结果

[hadoop@master1 sbin]$ hdfs dfs -cat output/*

1 dfsmetrics

1 dfsadmin

把结果取到本地

[hadoop@master1 sbin]$ hdfs dfs -get output /data

[hadoop@master1 sbin]$ ll /data

总用量 0

drwxrwxrwx. 5 hadoop hadoop 52 6月  12 19:20 hadoop

drwxrwxr-x. 2 hadoop hadoop 42 6月  12 23:03 output

[hadoop@master1 sbin]$ cat /data/output/*

1 dfsmetrics

1 dfsadmin

6、开启历史服务器

历史服务器服务用来在web中查看任务运行情况

[hadoop@master1 sbin]$ mr-jobhistory-daemon.sh start historyserver

starting historyserver, logging to /home/hadoop/hadoop/logs/mapred-hadoop-historyserver-master1.out

[hadoop@master1 sbin]$ jps

19985 Jps

15778 ResourceManager

15890 NodeManager

14516 NameNode

14827 SecondaryNameNode

19948 JobHistoryServer

14653 DataNode

在初学时尽可能的把配置简单化，有助于出错后的排查。

参考：

https://www.cnblogs.com/wangxin37/p/6501484.html

https://www.cnblogs.com/xing901022/p/5713585.html

文章题目：hadoop2.9.1伪分布式环境搭建以及文件系统的简单操作
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