Hadoop单机和伪分布式安装
本教程为单机版+伪分布式的Hadoop，安装过程写的有些简单，只作为笔记方便自己研究Hadoop用。
环境
| 操作系统 | Centos 6.5_64bit | |
| 本机名称 | hadoop001 | |
| 本机IP | 192.168.3.128 | |
| JDK | jdk-8u40-linux-x64.rpm | 点此下载 |
| Hadoop | 2.7.3 | 点此下载 |
Hadoop 有两个主要版本，Hadoop 1.x.y 和 Hadoop 2.x.y 系列，比较老的教材上用的可能是 0.20 这样的版本。Hadoop 2.x 版本在不断更新，本教程均可适用。如果需安装 0.20，1.2.1这样的版本，本教程也可以作为参考，主要差别在于配置项，配置请参考官网教程或其他教程。
单机安装
一、创建Hadoop用户
为了方便之后的操作，不干扰其他用户，咱们先建一个单独的Hadoop用户并设置密码[root@localhost ~]# useradd -m hadoop -s /bin/bash
[root@localhost ~]# passwd hadoop Changing password for user hadoop. New password: BAD PASSWORD: it is based on a dictionary word BAD PASSWORD: is too simple Retype new password: passwd: all authentication tokens updated successfully.
//还要修改host文件 [root@hadoop001 .ssh]# vim /etc/hosts 192.168.3.128 hadoop001
二、创建SSH无密码登录
单节点、集群都需要用到SSH登录，方便无障碍登录和通讯。
[hadoop@hadoop001 .ssh]$ cd ~/.ssh/ [hadoop@hadoop001 .ssh]$ ssh-keygen -t rsa Generating public/private rsa key pair. Enter file in which to save the key (/home/hadoop/.ssh/id_rsa): // 回车 Enter passphrase (empty for no passphrase): //回车 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: 97:75:b0:56:3b:57:8c:1f:b1:51:b6:d9:9f:77:f3:cf hadoop@hadoop001 The key's randomart image is: +--[ RSA 2048]----+ | . .=*| | +.+O| | + +=+| | + . o+| | S o o+| | . =| | .| | ..| | E| +-----------------+ [hadoop@hadoop001 .ssh]$ cat ./id_rsa.pub >> ./authorized_keys [hadoop@hadoop001 .ssh]$ ll total 12 -rw-rw-r--. 1 hadoop hadoop 398 Mar 14 14:09 authorized_keys -rw-------. 1 hadoop hadoop 1675 Mar 14 14:09 id_rsa -rw-r--r--. 1 hadoop hadoop 398 Mar 14 14:09 id_rsa.pub [hadoop@hadoop001 .ssh]$ chmod 644 authorized_keys [hadoop@hadoop001 .ssh]$ ssh hadoop001 Last login: Tue Mar 14 14:11:52 2017 from hadoop001
这样的话本机免密码登录已经配置成功了。
三、安装JDK
rpm -qa |grep java // 卸载所有出现的包 rpm -e --nodeps java-x.x.x-gcj-compat-x.x.x.x-40jpp.115 // 执行jdk-8u40-linux-x64.rpm包，不用配环境变量，不过需要加JAVA_HOME echo "JAVA_HOME"=/usr/java/latest/ >> /etc/environment
测试安装成功与否
[hadoop@hadoop001 soft]$ java -version java version "1.8.0_40" Java(TM) SE Runtime Environment (build 1.8.0_40-b25) Java HotSpot(TM) 64-Bit Server VM (build 25.40-b25, mixed mode)
四、安装Hadoop
//安装到opt目录下 [root@hadoop001 soft]# tar -zxf hadoop-2.7.3.tar.gz -C /opt/
修改目录权限
[root@hadoop001 opt]# ll total 20 drwxr-xr-x. 9 root root 4096 Aug 17 2016 hadoop-2.7.3 [root@hadoop001 opt]# chown -R hadoop:hadoop hadoop-2.7.3/ [root@hadoop001 opt]# ll total 20 drwxr-xr-x. 9 hadoop hadoop 4096 Aug 17 2016 hadoop-2.7.3
添加环境变量
[hadoop@hadoop001 bin]$ vim ~/.bash_profile # hadoop HADOOP_HOME=/opt/hadoop-2.7.3 PATH=$PATH:$HOME/bin:$HADOOP_HOME/bin:$HADOOP_HOME/sbin export PATH
测试安装成功与否
[hadoop@hadoop001 bin]$ hadoop
Usage: hadoop [--config confdir] [COMMAND | CLASSNAME]
CLASSNAME run the class named CLASSNAME
or
where COMMAND is one of:
fs run a generic filesystem user client
version print the version
jar <jar> run a jar file
note: please use "yarn jar" to launch
YARN applications, not this command.
checknative [-a|-h] check native hadoop and compression libraries availability
distcp <srcurl> <desturl> copy file or directories recursively
archive -archiveName NAME -p <parent path> <src>* <dest> create a hadoop archive
classpath prints the class path needed to get the
credential interact with credential providers
Hadoop jar and the required libraries
daemonlog get/set the log level for each daemon
trace view and modify Hadoop tracing settings
Most commands print help when invoked w/o parameters.
单词统计
创建输入文件夹input放输入文件
[root@hadoop001 /]# mkdir -p /data/input //创建测试文件word.txt [root@hadoop001 /]# vim word.txt Hi, This is a test file. Hi, I love hadoop and love you . //授权 [root@hadoop001 /]# chown hadoop:hadoop /data/input/word.txt //运行单词统计 [hadoop@hadoop001 hadoop-2.7.3]$ hadoop jar /opt/hadoop-2.7.3/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.3.jar wordcount /data/input/word.txt /data/output/ //...中间日志省略 17/03/14 15:22:44 INFO mapreduce.Job: Counters: 30 File System Counters FILE: Number of bytes read=592316 FILE: Number of bytes written=1165170 FILE: Number of read operations=0 FILE: Number of large read operations=0 FILE: Number of write operations=0 Map-Reduce Framework Map input records=3 Map output records=14 Map output bytes=114 Map output materialized bytes=127 Input split bytes=90 Combine input records=14 Combine output records=12 Reduce input groups=12 Reduce shuffle bytes=127 Reduce input records=12 Reduce output records=12 Spilled Records=24 Shuffled Maps =1 Failed Shuffles=0 Merged Map outputs=1 GC time elapsed (ms)=0 Total committed heap usage (bytes)=525336576 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=59 File Output Format Counters Bytes Written=85
执行成功，到output目录下看结果
[hadoop@hadoop001 output]$ vim part-r-00000
. 1 Hi, 2 I 1 This 1 a 1 and 1 file. 1 hadoop 1 is 1 love 2 test 1 you 1
【至此单机安装完成】
伪分布式安装
Hadoop 可以在单节点上以伪分布式的方式运行，Hadoop 进程以分离的 Java 进程来运行，节点既作为 NameNode 也作为 DataNode，同时，读取的是 HDFS 中的文件。
Hadoop 的配置文件位于 /$HADOOP_HOME/etc/hadoop/ 中，伪分布式至少需要修改2个配置文件 core-site.xml 和 hdfs-site.xml 。
Hadoop的配置文件是 xml 格式，每个配置以声明 property 的 name 和 value 的方式来实现。
修改core-site.xml
<configuration>
<property>
<name>hadoop.tmp.dir</name>
<value>file:/opt/hadoop-2.7.3/tmp</value>
<description>Abase for other temporary directories.</description>
</property>
<property>
<name>fs.defaultFS</name>
<value>hdfs://hadoop001:9000</value>
</property>
</configuration>
修改hdfs-site.xml
<configuration>
<property>
<name>dfs.replication</name>
<value>1</value>
</property>
<property>
<name>dfs.namenode.name.dir</name>
<value>file:/data/dfs/name</value>
</property>
<property>
<name>dfs.datanode.data.dir</name>
<value>file:/data/dfs/data</value>
</property>
</configuration>
伪分布式虽然只需要配置 fs.defaultFS 和 dfs.replication 就可以运行（官方教程如此），不过若没有配置 hadoop.tmp.dir 参数，则默认使用的临时目录为 /tmp/hadoo-hadoop，而这个目录在重启时有可能被系统清理掉，导致必须重新执行 format 才行。所以我们进行了设置，同时也指定 dfs.namenode.name.dir 和 dfs.datanode.data.dir，否则在接下来的步骤中可能会出错。
修改mapred-site.xml
文件默认不存在，只有一个模板，复制一份
[hadoop@hadoop001 hadoop]$ cp mared-site.xml.template mared-site.xml
configration下添加
<property>
<name>mapreduce.framework.name</name>
<value>yarn</value>
</property>
<property>
<name>mapreduce.jobhistory.address</name>
<value>master:10020</value>
</property>
<property>
<name>mapreduce.jobhistory.webapp.address</name>
<value>master:19888</value>
</property>
修改yarn-site.xml
<property> <name>yarn.nodemanager.aux-services</name> <value>mapreduce_shuffle</value> </property> <property> <name>yarn.nodemanager.aux-services.mapreduce.shuffle.class</name> <value>org.apache.hadoop.mapred.ShuffleHandler</value> </property> <property> <name>yarn.resourcemanager.address</name> <value>hadoop001:8032</value> </property> <property> <name>yarn.resourcemanager.scheduler.address</name> <value>hadoop001:8030</value> </property> <property> <name>yarn.resourcemanager.resource-tracker.address</name> <value>hadoop001:8035</value> </property> <property> <name>yarn.resourcemanager.admin.address</name> <value>hadoop001:8033</value> </property> <property> <name>yarn.resourcemanager.webapp.address</name> <value>hadoop001:8088</value> </property>
格式化namenode
[hadoop@hadoop001 hadoop]$ hdfs namenode –format
好，格式化后启动namenode和datanode的守护进程，发现报错
设置一下hadoop-env.sh文件，把${JAVA_HOME}替换成绝对路径
[hadoop@hadoop001 hadoop-2.7.3]$ vim etc/hadoop/hadoop-env.sh
export JAVA_HOME=/usr/java/jdk1.8.0_40/
重新启动start-dfs.sh + start-yarn.sh 或者 start-all.sh
守护进程已经成功启动了，证明配置伪分布式成功。
远程访问http://192.168.3.128:50070，发现无法访问，本地可以访问。
原因其实是修改了hadoop-env.sh 后没有重启格式化namenode，重新格式化后发现datanode启动不起来了。
最后，删除datanode数据文件下VERSION文件,格式化后重启就可以了。
