10.35.22.91
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注意：这个镜像中的root用户的密码是root
mkdir centos-ssh-root
cd centos-ssh-root
Vi Dockerfile
FROM centos
MAINTAINER jieranli <jieran.li@thomsonreuters.com>
RUN yum install -y openssh-server sudo
RUN sed -i 's/UsePAM yes/UsePAM no/g' /etc/ssh/sshd_config
RUN yum install -y openssh-clients
RUN echo "root:root" | chpasswd
RUN echo "root ALL=(ALL) ALL" >> /etc/sudoers
RUN ssh-keygen -t dsa -f /etc/ssh/ssh_host_dsa_key
RUN ssh-keygen -t rsa -f /etc/ssh/ssh_host_rsa_key
RUN mkdir /var/run/sshd
EXPOSE 22
CMD ["/usr/sbin/sshd", "-D"]
构建命令：
docker build -t centos-ssh-root:v1.0 .
查询刚才构建成功的镜像
docker images
2
mkdir centos-ssh-root-jdk
cd centos-ssh-root-jdk
Cp ../jdk-8u181-linux-x64.tar.gz .
Vi Dockerfile
FROM centos-ssh-root:v1.0
ADD jdk-8u181-linux-x64.tar.gz /usr/local/
RUN mv /usr/local/jdk1.8.0_181 /usr/local/jdk1.8
ENV JAVA_HOME /usr/local/jdk1.8
ENV PATH $JAVA_HOME/bin:$PATH
构建命令：
docker build -t centos-ssh-root-jdk:v2.0 .
查询构建成功的镜像
docker images
4
mkdir centos-ssh-root-jdk-hadoop
cd centos-ssh-root-jdk-hadoop
Cp ../hadoop-2.6.0-cdh5.5.2.tar.gz .
Vi Dockerfile
FROM centos-ssh-root-jdk:v2.0
ADD hadoop-2.6.0-cdh5.5.2.tar.gz /usr/local
RUN mv /usr/local/hadoop-2.6.0-cdh5.5.2 /usr/local/hadoop
ENV HADOOP_HOME /usr/local/hadoop
ENV PATH $HADOOP_HOME/bin:$PATH
构建命令：
docker build -t hadoop:v3.0 .
二：搭建hadoop分布式集群
1：集群规划
准备搭建一个具有三个节点的集群，一主两从
主节点：hadoop0 ip：10.35.22.11
从节点1：hadoop1 ip：10.35.22.12
从节点2：hadoop2 ip：10.35.22.13
但是由于docker容器重新启动之后ip会发生变化，所以需要我们给docker设置固定ip。使用pipework给docker容器设置固定ip
2：启动三个容器，分别作为hadoop0 hadoop1 hadoop2
在宿主机上执行下面命令，给容器设置主机名和容器的名称，并且在hadoop0中对外开放端口50070 和8088
docker run --name hadoop0 --hostname hadoop0 -d -P -p 50070:50070 -p 9000:9000 -p 50090:50090 -p 10020:10020 -p 19888:19888 -p 8088:8088 hadoop:v3.0
docker run --name hadoop1 --hostname hadoop1 -d -P hadoop:v3.0
docker run --name hadoop2 --hostname hadoop2 -d -P hadoop:v3.0
使用docker ps 查看刚才启动的是三个容器
3：给这三台容器设置固定IP
docker run -itd --name hadoop hadoop:v3.0 /bin/bash #生成容器
docker exec -it hadoop /bin/bash #进入正在运行的容器
1：下载pipework
下载地址：https://github.com/jpetazzo/pipework.git
2：把下载的zip包上传到宿主机服务器上，解压,改名字
docker cp pipework-master.zip hadoop:/work/pipework-master.zip
unzip pipework-master.zip
mv pipework-master pipework
cp -rp pipework/pipework /usr/local/bin/
3：安装bridge-utils
yum -y install bridge-utils
brctl show
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4：创建网络
sudo brctl addbr br1
sudo brctl delbr br0
brctl delif br0 veth1pl24213
sudo ip link set dev br1 up
sudo ip addr add 10.35.22.1/24 dev br1
5：给容器设置固定ip
pipework br0 hadoop0 10.35.22.11/24
pipework br0 hadoop1 10.35.22.12/24
pipework br0 hadoop2 10.35.22.15/24
验证一下，分别ping三个ip，能ping通就说明没问题
4：配置hadoop集群
先连接到hadoop0上，
使用命令
docker exec -it hadoop2 /bin/bash
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下面的步骤就是hadoop集群的配置过程
1：设置主机名与ip的映射，修改三台容器：vi /etc/hosts
添加下面配置
10.35.22.11 hadoop0
10.35.22.12 hadoop1
10.35.22.15 hadoop2
2：设置ssh免密码登录
在hadoop0上执行下面操作
cd ~
mkdir .ssh
cd .ssh
ssh-keygen -t rsa(一直按回车即可)
ssh-copy-id -i localhost
ssh-copy-id -i hadoop0
ssh-copy-id -i hadoop1
ssh-copy-id -i hadoop2
在hadoop1上执行下面操作
docker exec -it hadoop1 /bin/bash
cd ~
cd .ssh
ssh-keygen -t rsa(一直按回车即可)
ssh-copy-id -i localhost
ssh-copy-id -i hadoop0
ssh-copy-id -i hadoop1
ssh-copy-id -i hadoop2
在hadoop2上执行下面操作
docker exec -it hadoop2 /bin/bash
cd ~
cd .ssh
ssh-keygen -t rsa(一直按回车即可)
ssh-copy-id -i localhost
ssh-copy-id -i hadoop0
ssh-copy-id -i hadoop1
ssh-copy-id -i hadoop2
3：在hadoop0上修改hadoop的配置文件
vi .bash_profile
export JAVA_HOME=/usr/local/jdk1.8
export JRE_HOME=${JAVA_HOME}/jre
export CLASSPATH=.:${JAVA_HOME}/lib:${JRE_HOME}/lib
export PATH=${JAVA_HOME}/bin:$PATH
HADOOP_HOME=/usr/local/hadoop
HADOOP_CONF_DIR=$HADOOP_HOME/etc/hadoop
PATH=$HADOOP_HOME/bin:$PATH
export HADOOP_HOME HADOOP_CONF_DIR PATH
进入到/usr/local/hadoop/etc/hadoop目录
修改目录下的配置文件core-site.xml、hdfs-site.xml、yarn-site.xml、mapred-site.xml
(1)hadoop-env.sh
export JAVA_HOME=/usr/local/jdk1.8
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(2)core-site.xml
<configuration>
<property>
<name>fs.defaultFS</name>
<value>hdfs://hadoop0:9000</value>
</property>
<property>
<name>hadoop.tmp.dir</name>
<value>/usr/local/hadoop/tmp</value>
</property>
<property>
<name>io.file.buffer.size</name>
<value>131072</value>
<description>Size of read/write buffer used inSequenceFiles.</description>
</property>
<property>
<name>fs.trash.interval</name>
<value>1440</value>
</property>
</configuration>
(3)hdfs-site.xml
mkdir -p dfs/name
mkdir -p dfs/data
mkdir -p dfs/namesecondary
<configuration>
<property>
<name>dfs.namenode.secondary.http-address</name>
<value>hadoop0:50090</value>
<description>The secondary namenode http server address andport.</description>
</property>
<property>
<name>dfs.namenode.name.dir</name>
<value>file:///usr/local/hadoop/dfs/name</value>
<description>Path on the local filesystem where the NameNodestores the namespace and transactions logs persistently.</description>
</property>
<property>
<name>dfs.datanode.data.dir</name>
<value>file:///usr/local/hadoop/dfs/data</value>
<description>Comma separated list of paths on the local filesystemof a DataNode where it should store its blocks.</description>
</property>
<property>
<name>dfs.namenode.checkpoint.dir</name>
<value>file:///usr/local/hadoop/dfs/namesecondary</value>
<description>Determines where on the local filesystem the DFSsecondary name node should store the temporary images to merge. If this is acomma-delimited list of directories then the image is replicated in all of thedirectories for redundancy.</description>
</property>
<property>
<name>dfs.replication</name>
<value>2</value>
</property>
</configuration>
(4)yarn-site.xml
<configuration>
<property>
<name>yarn.resourcemanager.hostname</name>
<value>hadoop0</value>
<description>The hostname of theRM.</description>
</property>
<property>
<name>yarn.nodemanager.aux-services</name>
<value>mapreduce_shuffle</value>
<description>Shuffle service that needs to be set for Map Reduceapplications.</description>
</property>
<property>
<name>yarn.log-aggregation-enable</name>
<value>true</value>
</property>
</configuration>
(5)修改文件名：mv mapred-site.xml.template mapred-site.xml
vi mapred-site.xml
<property>
<name>mapreduce.framework.name</name>
<value>yarn</value>
<description>Theruntime framework for executing MapReduce jobs. Can be one of local, classic oryarn.</description>
</property>
<property>
<name>mapreduce.jobhistory.address</name>
<value>hadoop0:10020</value>
<description>MapReduce JobHistoryServer IPC host:port</description>
</property>
<property>
<name>mapreduce.jobhistory.webapp.address</name>
<value>hadoop0:19888</value>
<description>MapReduce JobHistoryServer Web UI host:port</description>
</property>
(6)格式化
进入到/usr/local/hadoop目录下
1、执行格式化命令
bin/hdfs namenode -format
格式化操作不能重复执行。如果一定要重复格式化，带参数-force即可。
(7)启动伪分布hadoop
命令：sbin/start-all.sh
1
第一次启动的过程中需要输入yes确认一下。
这里写图片描述
使用jps，检查进程是否正常启动？能看到下面几个进程表示伪分布启动成功
[root@hadoop0 hadoop]# jps
3267 SecondaryNameNode
3003 NameNode
3664 Jps
3397 ResourceManager
3090 DataNode
3487 NodeManager
(8)停止伪分布hadoop
命令：sbin/stop-all.sh
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(9)指定nodemanager的地址，修改文件yarn-site.xml
<property>
<description>The hostname of the RM.</description>
<name>yarn.resourcemanager.hostname</name>
<value>hadoop0</value>
</property>
(10)修改hadoop0中hadoop的一个配置文件etc/hadoop/slaves
删除原来的所有内容，修改为如下
hadoop1
hadoop2
(11)在hadoop0中执行命令-q不显示传输精度条
scp -rq /usr/local/hadoop hadoop1:/usr/local
scp -rq /usr/local/hadoop hadoop2:/usr/local
1
2
(12)启动hadoop分布式集群服务
sbin/start-all.sh
(13)验证集群是否正常
首先查看进程：
Hadoop0上需要有这几个进程
[root@hadoop0 hadoop]# jps
4643 Jps
4073 NameNode
4216 SecondaryNameNode
4381 ResourceManager
Hadoop1上需要有这几个进程
[root@hadoop1 hadoop]# jps
715 NodeManager
849 Jps
645 DataNode
Hadoop2上需要有这几个进程
[root@hadoop2 hadoop]# jps
456 NodeManager
589 Jps
388 DataNode
hadoop fs -put
hdfs dfs -put aa.txt /
cd /usr/local/hadoop/share/hadoop/mapreduce
hadoop jar hadoop-mapreduce-examples-2.4.1.jar wordcount /a.txt /out
通过浏览器访问集群的服务
由于在启动hadoop0这个容器的时候把50070和8088映射到宿主机的对应端口上了
adb9eba7142b crxy/centos-ssh-root-jdk-hadoop "/usr/sbin/sshd -D" About an hour ago Up About an hour 0.0.0.0:8088->8088/tcp, 0.0.0.0:50070->50070/tcp, 0.0.0.0:32770->22/tcp hadoop0
1
所以在这可以直接通过宿主机访问容器中hadoop集群的服务
宿主机的ip为：10.35.22.92
http://10.35.22.92:50070/
http://10.35.22.92:19888/
三：集群节点重启
停止三个容器，在宿主机上执行下面命令
docker stop hadoop0
docker stop hadoop1
docker stop hadoop2
容器停止之后，之前设置的固定ip也会消失，重新再使用这几个容器的时候还需要重新设置固定ip
先把之前停止的三个容器起来
docker start hadoop0
docker start hadoop1
docker start hadoop2
在宿主机上执行下面命令重新给容器设置固定ip
pipework br0 hadoop0 10.35.22.11/24
pipework br0 hadoop1 10.35.22.12/24
pipework br0 hadoop2 10.35.22.15/24
还需要重新在容器中配置主机名和ip的映射关系，每次都手工写比较麻烦
写一个脚本，runhosts.sh
#!/bin/bash
echo 10.35.22.11 hadoop0 > /etc/hosts
echo 10.35.22.12 hadoop1 > /etc/hosts
echo 10.35.22.15 hadoop2 > /etc/hosts
添加执行权限，chmod +x runhosts.sh
把这个脚本拷贝到所有节点，并且分别执行这个脚本
scp runhosts.sh hadoop1:~
scp runhosts.sh hadoop2:~
1
2
执行脚本的命令 ./runhosts.sh
查看/etc/hosts文件中是否添加成功
这里写图片描述
注意：有一些docker版本中不会在hosts文件中自动生成下面这些映射，所以我们才在这里手工给容器设置固定ip，并设置主机名和ip的映射关系。
172.17.0.25 hadoop0
172.17.0.25 hadoop0.bridge
172.17.0.26 hadoop1
172.17.0.26 hadoop1.bridge
172.17.0.27 hadoop2
172.17.0.27 hadoop2.bridge
启动hadoop集群
sbin/start-all.sh