hadoop2.4.1伪分布式环境搭建

　　注意:所有的安装用普通哟用户安装，所以首先使普通用户可以以sudo执行一些命令:

0.虚拟机中前期的网络配置参考:

　　http://www.cnblogs.com/qlqwjy/p/7783253.html

1.赋予hadoop用户以sudo执行一些命令

visodo
或者
 vim /etc/sudoers

添加下面第二行内容:

登录hadoop用户查看命令:

[hadoop@localhost java]$ sudo -l　　#查看当前用户可以以sudo命令执行哪些命令
Matching Defaults entries for hadoop on this host:
    requiretty, !visiblepw, always_set_home, env_reset, env_keep="COLORS DISPLAY HOSTNAME HISTSIZE INPUTRC KDEDIR
    LS_COLORS", env_keep+="MAIL PS1 PS2 QTDIR USERNAME LANG LC_ADDRESS LC_CTYPE", env_keep+="LC_COLLATE
    LC_IDENTIFICATION LC_MEASUREMENT LC_MESSAGES", env_keep+="LC_MONETARY LC_NAME LC_NUMERIC LC_PAPER LC_TELEPHONE",
    env_keep+="LC_TIME LC_ALL LANGUAGE LINGUAS _XKB_CHARSET XAUTHORITY", secure_path=/sbin\:/bin\:/usr/sbin\:/usr/bin

User hadoop may run the following commands on this host:
    (ALL) ALL

 ------------------------安装hadoop运行环境，切换到hadoop用户----------------------

　　我所有的文件上传采用的sftp，建议安装git工具自带ssh和sftp等。注意自己的linux位数，我刚开始安装的64位JDK，结果linux是32位，JDK不能用

查看位数:

uname -a
或者
getconf LONG_BIT

1.安装JDK

(1)上传到服务器之后解压

sudo tar -zxvf ./jdk-7u65-linux-i586.tar.gz 

(2)查看当前安装目录:

[hadoop@localhost jdk1..0_65]$ pwd
/opt/java/jdk1..0_65

(3)配置环境变量 ;

[hadoop@localhost jdk1..0_65]$ tail - ~/.bashrc
export JAVA_HOME=/opt/java/jdk1..0_65
export JRE_HOME=${JAVA_HOME}/jre
export CLASSPATH=.:${JAVA_HOME}/lib:${JRE_HOME}/lib
export PATH=${JAVA_HOME}/bin:${PATH}

重新加载环境变量:

[hadoop@localhost jdk1..0_65]$ source ~/.bashrc 

 (4)执行java或者javac测试:

[hadoop@localhost jdk1..0_65]$ java -vsersion
Unrecognized option: -vsersion
Error: Could not create the Java Virtual Machine.
Error: A fatal exception has occurred. Program will exit.
[hadoop@localhost jdk1..0_65]$ javac -version
javac 1.7.0_65

2. 安装hadoop2.4.1

(1)将文件上传到服务器

sftp> put hadoop-2.4..tar.gz

(2)解压

sudo tar -zxvf ./hadoop-2.4..tar.gz

(3)解压后查看目录:

[hadoop@localhost hadoop-2.4.]$ ls
bin  etc  include  lib  libexec  LICENSE.txt  NOTICE.txt  README.txt  sbin  share

　　其中java相关的jar包存放在share目录，下面还有个docs目录，没啥用，删掉就行了。

　　bin是可执行文件

　　etc是hadoop是相关配置文件

　　lib,libexec是相关的本地服务

　　sbin是hadoop的管理执行文件

 

(4)修改配置文件:hadoop2.x的配置文件$HADOOP_HOME/etc/hadoop

• 修改:hadoop-env.sh(设置JDK环境变量)

#第27行

export JAVA_HOME=/opt/java/jdk1..0_65

• 修改:core-site.xml

        <!-- 指定HADOOP所使用的文件系统schema（URI），HDFS的老大（NameNode）的地址 -->
        <property>
            <name>fs.defaultFS</name>
            <value>hdfs://localhost:9000</value>
        </property>
        <!-- 指定hadoop运行时产生文件的存储目录 -->
        <property>
            <name>hadoop.tmp.dir</name>
            <value>/opt/hadoop/hadoop-2.4.1/data/</value>
       </property>

• 修改hdfs-site.xml   hdfs-default.xml

        <!-- 指定HDFS副本的数量 -->
        <property>
            <name>dfs.replication</name>
            <value>1</value>
  　　  </property>

• • 修改   mapred-site.xml　　(mapreduce)

首先将mapred-site.xml.template改名字为mapred-site.xml。否则hadoop不会读取

[hadoop@localhost hadoop]$ sudo mv ./mapred-site.xml.template ./mapred-site.xml

修改:

        <!-- 指定mapreduce运行在yarn上 -->
        <property>
            <name>mapreduce.framework.name</name>
            <value>yarn</value>
   　　 </property>
        

• • 修改 yarn-site.xml　　(修改yarn)

        <!-- 指定YARN的老大（ResourceManager）的地址 -->
        <property>
            <name>yarn.resourcemanager.hostname</name>
            <value>localhost</value>
　　    </property>
        <!-- reducer获取数据的方式 -->
   　　 <property>
            <name>yarn.nodemanager.aux-services</name>
            <value>mapreduce_shuffle</value>
     　</property>

(5)关闭linux的防火墙:

[root@localhost ~]# service iptables stop　　#关闭防火墙
iptables: Flushing firewall rules: [  OK  ]
iptables: Setting chains to policy ACCEPT: filter [  OK  ]
iptables: Unloading modules: [  OK  ]
[root@localhost ~]# ls
anaconda-ks.cfg  install.log  install.log.syslog
[root@localhost ~]# service iptables status　　#查看iptables状态
iptables: Firewall is not running.

3.启动hadoop与测试hadoop

(1)前期准备

• 首先将hadoop添加到环境变量，便于在任意目录使用hadoop的命令:

export JAVA_HOME=/opt/java/jdk1..0_65
export HADOOP_HOME=/opt/hadoop/hadoop-2.4.1
export JRE_HOME=${JAVA_HOME}/jre
export CLASSPATH=.:${JAVA_HOME}/lib:${JRE_HOME}/lib
export PATH=${JAVA_HOME}/bin:${PATH}:${HADOOP_HOME}/bin:${HADOOP_HOME}/sbin

• 格式化namenode（是对namenode进行初始化）

hdfs namenode -format (hadoop namenode -format)

执行命令之后会在我们的配置的hadoop的临时目录下面创建  dfs/name/current/    目录并且写入四个文件:

[root@localhost data]# ll ./dfs/name/current/
total
-rw-r--r--.  root root  Apr  : fsimage_0000000000000000000
-rw-r--r--.  root root   Apr  : fsimage_0000000000000000000.md5
-rw-r--r--.  root root    Apr  : seen_txid
-rw-r--r--.  root root  Apr  : VERSION

(2)启动hadoop(最好设置ssh秘钥登录，否则会输入多次密码，可以自己写个shell脚本调用hdfs和yarn两个ssh脚本)

• 启动HDFS

先启动HDFS，到hadoop安装目录下:　　/opt/hadoop/hadoop-2.4.1/sbin
　　

sbin/start-dfs.sh

验证是否启动成功

[root@localhost sbin]# jps
 SecondaryNameNode
 Jps
 DataNode
 NameNode

解释:　　上面启动hadoop的时候会读取启动localhost的Namenode，因为hadoop的安装目录下的etc下有个slaves文件，指定从哪些机器启动Namenode

如果搭建多个节点需要在下面的配置文件增加节点，正规的分布式集群

[root@localhost hadoop]# cat ./slaves
localhost

• 启动yarn

[root@localhost sbin]# ./start-yarn.sh

再次查看:

[root@localhost sbin]# jps
 NodeManager
 ResourceManager
 SecondaryNameNode
 DataNode
 Jps
 NameNode

(3)测试上面启动的hdfs和yarn

http://192.168.2.136:50070 （HDFS管理界面）
http://192.168.2.136:8088 （MR管理界面）

• 测试hdfs

我们也可以通过网页浏览hafs文件:

首先我们上传一个文件:

[root@localhost ~]# ll
total
-rw-------.  root root   Sep     anaconda-ks.cfg
-rw-r--r--.  root root  Sep     install.log
-rw-r--r--.  root root   Sep     install.log.syslog
[root@localhost ~]# hadoop fs -put install.log hdfs://localhost:9000/　　#将当前目录下的install.log上传到hsfs的根目录下

 接下来我们再次查看数据会发现:

点开也可以下载文件:

我们在本地删掉install.log然后从hdfs中下载文件:

[root@localhost ~]# rm -rf ./install.log　　#删除文件
[root@localhost ~]# ls
anaconda-ks.cfg  install.log.syslog

[root@localhost ~]# hadoop fs -get hdfs://localhost:9000/install.log　　#hadoop下载文件
[root@localhost ~]# ls　　
anaconda-ks.cfg  install.log  install.log.syslo

• 测试mapreduce

由于我们没有编写mapreduce程序，所以我们需要利用hadoop自带的一些程序进行测试，下面测试一个求PI的值和一个统计单词出现次数的mapreduce程序

进入到hadoop的mapreduce目录下:

[root@localhost mapreduce]# pwd
/opt/hadoop/hadoop-2.4./share/hadoop/mapreduce

例一:计算求pi值的mapreduce程序

[root@localhost mapreduce]# hadoop jar hadoop-mapreduce-examples-2.4.1.jar pi 5 5　　#执行求pi值的mapreduce，开启5个map，每个map取样5个
Number of Maps  = 5
Samples per Map = 5
Wrote input for Map #
Wrote input for Map #
Wrote input for Map #
Wrote input for Map #
Wrote input for Map #
Starting Job
// :: INFO client.RMProxy: Connecting to ResourceManager at localhost/127.0.0.1:
// :: INFO input.FileInputFormat: Total input paths to process :
// :: INFO mapreduce.JobSubmitter: number of splits:
// :: INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1523441540916_0001
// :: INFO impl.YarnClientImpl: Submitted application application_1523441540916_0001
// :: INFO mapreduce.Job: The url to track the job: http://localhost:8088/proxy/application_1523441540916_0001/
// :: INFO mapreduce.Job: Running job: job_1523441540916_0001
// :: INFO mapreduce.Job: Job job_1523441540916_0001 running in uber mode : false
// :: INFO mapreduce.Job:  map % reduce %
// :: INFO mapreduce.Job:  map % reduce %
// :: INFO mapreduce.Job:  map % reduce %
// :: INFO mapreduce.Job:  map % reduce %
// :: INFO mapreduce.Job:  map % reduce %
// :: INFO mapreduce.Job: Job job_1523441540916_0001 completed successfully
// :: INFO mapreduce.Job: Counters:
        File System Counters
                FILE: Number of bytes read=
                FILE: Number of bytes written=
                FILE: Number of read operations=
                FILE: Number of large read operations=
                FILE: Number of write operations=
                HDFS: Number of bytes read=
                HDFS: Number of bytes written=
                HDFS: Number of read operations=
                HDFS: Number of large read operations=
                HDFS: Number of write operations=
        Job Counters
                Launched map tasks=
                Launched reduce tasks=
                Data-local map tasks=
                Total time spent by all maps in occupied slots (ms)=
                Total time spent by all reduces in occupied slots (ms)=
                Total time spent by all map tasks (ms)=
                Total time spent by all reduce tasks (ms)=
                Total vcore-seconds taken by all map tasks=
                Total vcore-seconds taken by all reduce tasks=
                Total megabyte-seconds taken by all map tasks=
                Total megabyte-seconds taken by all reduce tasks=
        Map-Reduce Framework
                Map input records=
                Map output records=
                Map output bytes=
                Map output materialized bytes=
                Input split bytes=
                Combine input records=
                Combine output records=
                Reduce input groups=
                Reduce shuffle bytes=
                Reduce input records=
                Reduce output records=
                Spilled Records=
                Shuffled Maps =
                Failed Shuffles=
                Merged Map outputs=
                GC time elapsed (ms)=
                CPU time spent (ms)=
                Physical memory (bytes) snapshot=
                Virtual memory (bytes) snapshot=
                Total committed heap usage (bytes)=
        Shuffle Errors
                BAD_ID=
                CONNECTION=
                IO_ERROR=
                WRONG_LENGTH=
                WRONG_MAP=
                WRONG_REDUCE=
        File Input Format Counters
                Bytes Read=
        File Output Format Counters
                Bytes Written=
Job Finished in 188.318 seconds
Estimated value of Pi is 3.68000000000000000000　　#计算结果

例二:一个wordcount的mapreduce(给一篇英文文章，会统计每个单词出现的次数)

(1)编辑一个英文文件

[root@localhost mapreduce]# cat ./test.txt
hello lll
hello kkk
hello meinv
hello 

(2)为了计算我们需要将文件上传到hdfs中

先在hdfs中建一个目录:(两种创建目录的方式)

[root@localhost mapreduce]# hadoop fs -mkdir hdfs://localhost:9000/wordcount　　#第一种
[root@localhost mapreduce]# hadoop fs -mkdir /wordcount/input　　　　　　　　　　#第二种。/是相对于hdfs的根目录

然后我们可以在hdfs的web管理中看到目录:(其中tmp和user是我们执行上一个程序产生的目录)

 接下来我们将上面的英文文件上传到hdfs的wordcount/input/目录下

[root@localhost mapreduce]# hadoop fs -put test.txt /wordcount/input

从web中查看目录;

测试wordcount程序:(mapreduce启动很慢，因为要启动很多程序)

测试统计hdfs的/wordcount/input目录下的所有的文件，并将统计结果输出到/wordcount/output目录中，/是hdfs的根目录

[root@localhost mapreduce]# hadoop jar hadoop-mapreduce-examples-2.4..jar wordcount /wordcount/input /wordcount/output
// :: INFO client.RMProxy: Connecting to ResourceManager at localhost/127.0.0.1:
// :: INFO input.FileInputFormat: Total input paths to process :
// :: INFO mapreduce.JobSubmitter: number of splits:
// :: INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1523441540916_0002
// :: INFO impl.YarnClientImpl: Submitted application application_1523441540916_0002
// :: INFO mapreduce.Job: The url to track the job: http://localhost:8088/proxy/application_1523441540916_0002/
// :: INFO mapreduce.Job: Running job: job_1523441540916_0002
// :: INFO mapreduce.Job: Job job_1523441540916_0002 running in uber mode : false
// :: INFO mapreduce.Job:  map % reduce %
// :: INFO mapreduce.Job:  map % reduce %
// :: INFO mapreduce.Job:  map % reduce %
// :: INFO mapreduce.Job: Job job_1523441540916_0002 completed successfully
// :: INFO mapreduce.Job: Counters:
        File System Counters
                FILE: Number of bytes read=
                FILE: Number of bytes written=
                FILE: Number of read operations=
                FILE: Number of large read operations=
                FILE: Number of write operations=
                HDFS: Number of bytes read=
                HDFS: Number of bytes written=
                HDFS: Number of read operations=
                HDFS: Number of large read operations=
                HDFS: Number of write operations=
        Job Counters
                Launched map tasks=
                Launched reduce tasks=
                Data-local map tasks=
                Total time spent by all maps in occupied slots (ms)=
                Total time spent by all reduces in occupied slots (ms)=
                Total time spent by all map tasks (ms)=
                Total time spent by all reduce tasks (ms)=
                Total vcore-seconds taken by all map tasks=
                Total vcore-seconds taken by all reduce tasks=
                Total megabyte-seconds taken by all map tasks=
                Total megabyte-seconds taken by all reduce tasks=
        Map-Reduce Framework
                Map input records=
                Map output records=
                Map output bytes=
                Map output materialized bytes=
                Input split bytes=
                Combine input records=
                Combine output records=
                Reduce input groups=
                Reduce shuffle bytes=
                Reduce input records=
                Reduce output records=
                Spilled Records=
                Shuffled Maps =
                Failed Shuffles=
                Merged Map outputs=
                GC time elapsed (ms)=
                CPU time spent (ms)=
                Physical memory (bytes) snapshot=
                Virtual memory (bytes) snapshot=
                Total committed heap usage (bytes)=
        Shuffle Errors
                BAD_ID=
                CONNECTION=
                IO_ERROR=
                WRONG_LENGTH=
                WRONG_MAP=
                WRONG_REDUCE=
        File Input Format Counters
                Bytes Read=
        File Output Format Counter

 查看hdfs的/wordcount/output目录下的文件信息:

[root@localhost mapreduce]# hadoop fs -ls /wordcount/output　　查看目录信息
Found  items
-rw-r--r--    root supergroup           -- : /wordcount/output/_SUCCESS
-rw-r--r--    root supergroup          -- : /wordcount/output/part-r-

 查看统计结果文件信息:

[root@localhost mapreduce]# hadoop fs -cat /wordcount/output/part-r-
hello
kkk
lll
meinv   

 也可以从web中下载查看: