公司Commerce Cloud平台上提供申请主机的服务。昨天试了下，申请了3台机器，搭了个hadoop环境。以下是机器的一些配置：
emi-centos-6.4-x86_64
medium | 6GB 内存| 2 虚拟内核 | 30.0GB 盘
3个机器的主机和ip规划如下：
IP地址 主机名 用途
192.168.0.101 hd1 namenode
192.168.0.102 hd2 datanode
192.168.0.103 hd3 datanode
一、系统设置
（所有步骤都需要在所有节点执行）
1. 修改主机名及ip地址解析
1) 修改主机名
[root@hd1 toughhou]# hostname hd1 [root@hd1 toughhou]# cat /etc/sysconfig/network NETWORKING=yes HOSTNAME=hd1
2) 增加ip和主机映射
[root@hd1 toughhou]# vi /etc/hosts 127.0.0.1 localhost localhost.localdomain localhost4 localhost4.localdomain4 ::1 localhost localhost.localdomain localhost6 localhost6.localdomain6 192.168.0.101 hd1 192.168.0.102 hd2 192.168.0.103 hd3
3) 验证是否成功
[toughhou@hd1 ~]$ ping hd2 PING hd2 (192.168.0.102) 56(84) bytes of data. 64 bytes from hd2 (192.168.0.102): icmp_seq=1 ttl=63 time=2.55 ms [toughhou@hd1 ~]$ ping hd3 PING hd3 (192.168.0.103) 56(84) bytes of data. 64 bytes from hd3 (192.168.0.103): icmp_seq=1 ttl=63 time=2.48 ms
能ping通说明已经OK。
2. 关闭防火墙
[root@hd1 toughhou]# chkconfig iptables off
3. SSH免密码登陆
1) 生成密钥与公钥
登陆到hd1，把生成的id_rsa.pub（公钥）内容cat到authorized_keys文件中。同时登陆到hd2, hd3，生成id_rsa.pub，并把hd2, hd3各自的id_rsa.pub的内容copy到hd1中的authorzied_keys中。最后从hd1中scp到hd2, hd3的.ssh目录中。
[toughhou@hd1 ~]$ ssh-keygen -t rsa [toughhou@hd1 ~]$ cat id_rsa.pub >> authorized_keys [toughhou@hd2 ~]$ ssh-keygen -t rsa [toughhou@hd2 ~]$ cat id_rsa.pub >> authorized_keys [toughhou@hd3 ~]$ ssh-keygen -t rsa [toughhou@hd3 ~]$ cat id_rsa.pub >> authorized_keys
2) scp authorized_keys到hd2, hd3
[toughhou@hd1 ~]$ scp authorized_keys 192.168.0.102:/home/toughhou/.ssh/ [toughhou@hd1 ~]$ scp authorized_keys 192.168.0.103:/home/toughhou/.ssh/
3) 验证ssh登陆是否是免密码
（第一次需要密码，若配置正确的话之后就不用密码了。）
[toughhou@hd1 ~]$ ssh 192.168.0.102 [toughhou@hd2 ~]$ [toughhou@hd1 ~]$ ssh 192.168.0.103 [toughhou@hd3 ~]$
关于SSH免密码登陆，也可以参考文章 “SSH时不需输入密码”，它更具体地说了关于SSH设置。
二、安装jdk、hadoop及设置环境变量
1. 下载jdk、hadoop安装包
download.oracle.com/otn-pub/java/jdk/7u65-b17/jdk-7u65-linux-x64.tar.gz
http://mirrors.cnnic.cn/apache/hadoop/common/hadoop-2.4.0/hadoop-2.4.0.tar.gz
2. 解压
[toughhou@hd1 software]$ tar zxvf jdk-7u65-linux-x64.gz [toughhou@hd1 software]$ tar zxvf hadoop-2.4.0.tar.gz [root@hd1 software]# mv hadoop-2.4.0 /opt/hadoop-2.4.0 [root@hd1 software]# mv jdk1.7.0_65 /opt/jdk1.7.0
3. 设置Java环境变量
以root用户登陆编辑/etc/profile，加入以下内容：
[root@hd1 software]# vi /etc/profile #java export JAVA_HOME=/opt/jdk1.7.0 export JRE_HOME=$JAVA_HOME/jre export PATH=$PATH:$JAVA_HOME/bin export CLASSPATH=./:$JAVA_HOME/lib:$JAVA_HOME/jre/lib #hadoop export HADOOP_HOME=/opt/hadoop-2.4.0 export HADOOP_COMMON_HOME=$HADOOP_HOME export HADOOP_HDFS_HOME=$HADOOP_HOME export HADOOP_MAPRED_HOME=$HADOOP_HOME export HADOOP_YARN_HOME=$HADOOP_HOME export HADOOP_CONF_DIR=$HADOOP_HOME/etc/hadoop export PATH=$PATH:$HADOOP_HOME/bin:$HADOOP_HOME/sbin:$HADOOP_HOME/lib export HADOOP_COMMON_LIB_NATIVE_DIR=$HADOOP_HOME/lib/native export HADOOP_OPTS="-Djava.library.path=$HADOOP_HOME/lib" export JAVA_LIBRARY_PATH=$HADOOP_HOME/lib/native
4. 验证环境变量
[toughhou@hd1 hadoop]$ java -version
[toughhou@hd1 hadoop]$ hadoop
Usage: hadoop [--config confdir] COMMAND
三、hadoop集群设置
1. 修改hadoop配置文件
[toughhou@hd1 hadoop]$ cd /opt/hadoop-2.4.0/etc/hadoop
1) hadoop-env.sh、yarn-env.sh 设置JAVA_HOME环境变量
最开始以为已经在/etc/profile设置了JAVA_HOME，所以在hadoop-env.sh和yarn-env.sh中已经能成功获取到JAVA_HOME，所以就不用再设置了。最终发现这在hadoop-2.4.0中行不通，start-all.sh的时候出错了（hd1: Error: JAVA_HOME is not set and could not be found.）。
找到里面的JAVA_HOME,修改为实际路径
2) slaves
这个文件配置所有datanode节点，以便namenode搜索
[toughhou@hd1 hadoop]$ vi slaves hd2 hd3
3) core-site.xml
<configuration> <property> <name>fs.defaultFS</name> <value>hdfs://hd1:9000</value> </property> <property> <name>io.file.buffer.size</name> <value>131072</value> </property> <property> <name>hadoop.tmp.dir</name> <value>/hadoop/temp</value> <description>A base for other temporary directories.</description> </property> <property> <name>hadoop.proxyuser.root.hosts</name> <value>hd1</value> </property> <property> <name>hadoop.proxyuser.root.groups</name> <value>*</value> </property> </configuration>
4) hdfs-site.xml
<configuration> <property> <name>dfs.namenode.name.dir</name> <value>/hadoop/name</value> <final>true</final> </property> <property> <name>dfs.datanode.data.dir</name> <value>/hadoop/data</value> <final>true</final> </property> <property> <name>dfs.replication</name> <value>2</value> </property> <property> <name>dfs.permissions</name> <value>false</value> </property> </configuration>
5) mapred-site.xml
<configuration> <property> <name>fs.defaultFS</name> <value>hdfs://hd1:9000</value> </property> <property> <name>io.file.buffer.size</name> <value>131072</value> </property> <property> <name>hadoop.tmp.dir</name> <value>/hadoop/temp</value> <description>A base for other temporary directories.</description> </property> <property> <name>hadoop.proxyuser.root.hosts</name> <value>hd1</value> </property> <property> <name>hadoop.proxyuser.root.groups</name> <value>*</value> </property> </configuration>
6) yarn-site.xml
<configuration> <property> <name>yarn.resourcemanager.address</name> <value>hd1:18040</value> </property> <property> <name>yarn.resourcemanager.scheduler.address</name> <value>hd1:18030</value> </property> <property> <name>yarn.resourcemanager.resource-tracker.address</name> <value>hd1:18025</value> </property> <property> <name>yarn.resourcemanager.admin.address</name> <value>hd1:18041</value> </property> <property> <name>yarn.resourcemanager.webapp.address</name> <value>hd1:8088</value> </property> <property> <name>yarn.nodemanager.local-dirs</name> <value>/hadoop/mynode/my</value> </property> <property> <name>yarn.nodemanager.log-dirs</name> <value>/hadoop/mynode/logs</value> </property> <property> <name>yarn.nodemanager.log.retain-seconds</name> <value>10800</value> </property> <property> <name>yarn.nodemanager.remote-app-log-dir</name> <value>/logs</value> </property> <property> <name>yarn.nodemanager.remote-app-log-dir-suffix</name> <value>logs</value> </property> <property> <name>yarn.log-aggregation.retain-seconds</name> <value>-1</value> </property> <property> <name>yarn.log-aggregation.retain-check-interval-seconds</name> <value>-1</value> </property> <property> <name>yarn.nodemanager.aux-services</name> <value>mapreduce_shuffle</value> </property> </configuration>
2. 把以下文件复制到其它节点
[root@hd1 toughhou]# scp -R /opt/hadoop-2.4.0/ hd2:/opt/ [root@hd1 toughhou]# scp -R /opt/hadoop-2.4.0/ hd3:/opt/ [root@hd1 toughhou]# scp -R /opt/jdk1.7.0/ hd2:/opt/ [root@hd1 toughhou]# scp -R /opt/jdk1.7.0/ hd3:/opt/ [root@hd1 toughhou]# scp /etc/profile hd2:/etc/profile [root@hd1 toughhou]# scp /etc/profile hd3:/etc/profile [root@hd1 toughhou]# scp /etc/hosts hd2:/etc/hosts [root@hd1 toughhou]# scp /etc/hosts hd3:/etc/hosts
配置完成之后需要重启电脑
3. namenode初始化
只需要第一次的时候初始化，之后就不需要了
[toughhou@hd1 bin]$ hdfs namenode -format
如果“Exiting with status 0”，就说明OK。
14/07/23 03:26:33 INFO util.ExitUtil: Exiting with status 0
4. 启动集群
[toughhou@hd1 sbin]$ cd /opt/hadoop-2.4.0/sbin [toughhou@hd1 sbin]$ ./start-all.sh
This script is Deprecated. Instead use start-dfs.sh and start-yarn.sh Starting namenodes on [hd1] hd1: namenode running as process 12580. Stop it first. hd2: starting datanode, logging to /opt/hadoop-2.4.0/logs/hadoop-toughhou-datanode-hd2.out hd3: starting datanode, logging to /opt/hadoop-2.4.0/logs/hadoop-toughhou-datanode-hd3.out Starting secondary namenodes [0.0.0.0] 0.0.0.0: secondarynamenode running as process 12750. Stop it first. starting yarn daemons resourcemanager running as process 11900. Stop it first. hd3: starting nodemanager, logging to /opt/hadoop-2.4.0/logs/yarn-toughhou-nodemanager-hd3.out hd2: starting nodemanager, logging to /opt/hadoop-2.4.0/logs/yarn-toughhou-nodemanager-hd2.out
5. 查看各节点的状态
[toughhou@hd1 sbin]$ jps 16358 NameNode 16535 SecondaryNameNode 16942 Jps 16683 ResourceManage [toughhou@hd2 ~]$ jps 2253 NodeManager 2369 Jps 2152 DataNode [toughhou@hd3 ~]$ jps 2064 NodeManager 2178 Jps 1963 DataNode
以上说明都OK。
6. windows添加快捷访问
为了方便访问，我们也可以编辑 %systemroot%system32driversetchosts 文件，加入以下的 ip和主机映射
192.168.0.101 hd1 192.168.0.102 hd2 192.168.0.103 hd3
这样，我们在自己机器上也可以通过 http://hd2:8042/node 方式访问节点，而没必要用 http://192.168.0.102:8042/node。
7. wordcount 测试
为了更进一步验证hadoop环境，我们可以运行hadoop自带的例子。
wordcount是hadoop最经典的mapreduce例子。我们进入到相应目录运行自带的jar包，来测试hadoop环境是否OK。
具体步骤：
1) hdfs上创建目录
[toughhou@hd1 ~]$ hadoop fs -mkdir /in/wordcount [toughhou@hd1 ~]$ hadoop fs -mkdir /out/
2) 上传文件到hdfs
[toughhou@hd1 ~]$ cat in1.txt Hello World , Hello China, Hello Shanghai I love China How are you [toughhou@hd1 ~]$ hadoop fs -put in1.txt /in/wordcount
3) 运行wordcount
[toughhou@hd1 ~]$ cd /opt/hadoop-2.4.0/share/hadoop/mapreduce/ [toughhou@hd2 mapreduce]$ hadoop jar hadoop-mapreduce-examples-2.4.0.jar wordcount /in/wordcount /out/out1 14/07/23 10:42:36 INFO client.RMProxy: Connecting to ResourceManager at hd1/192.168.0.101:18040 14/07/23 10:42:38 INFO input.FileInputFormat: Total input paths to process : 2 14/07/23 10:42:38 INFO mapreduce.JobSubmitter: number of splits:2 14/07/23 10:42:38 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1406105556378_0003 14/07/23 10:42:38 INFO impl.YarnClientImpl: Submitted application application_1406105556378_0003 14/07/23 10:42:38 INFO mapreduce.Job: The url to track the job: http://hd1:8088/proxy/application_1406105556378_0003/ 14/07/23 10:42:38 INFO mapreduce.Job: Running job: job_1406105556378_0003 14/07/23 10:42:46 INFO mapreduce.Job: Job job_1406105556378_0003 running in uber mode : false 14/07/23 10:42:46 INFO mapreduce.Job: map 0% reduce 0% 14/07/23 10:42:55 INFO mapreduce.Job: map 100% reduce 0% 14/07/23 10:43:01 INFO mapreduce.Job: map 100% reduce 100%
4) 查看运行结果
[toughhou@hd2 mapreduce]$ hadoop fs -cat /out/out4/part-r-00000 , 1 China 1 China, 1 Hello 3 How 1 I 1 Shanghai 1 World 1 are 1 love 1 you 1
到此，全部结束。整个hadoop-2.4.0集群搭建过程全部结束。