安装BigTop编译的Hadoop3.1.1版本过程记录

最新推荐文章于 2025-07-20 13:23:23 发布

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最新推荐文章于 2025-07-20 13:23:23 发布

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分类专栏：大数据组件文章标签： hadoop yum bigtop rpm zookeeper

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本文记录安装编译后的rpm包的过程，主要是在不同主机上分配好需要安装的服务，使用yum命令安装，然后修改对应的配置文件，最后启动服务即可。

1、前期准备

主要包括主机的设置方面，配置主机名、IP和主机名映射、关闭防火墙等

#关闭防火墙
hostnamectl set-hostname hdp1

#修改主机名
172.16.25.139 hdp1
172.16.25.140 hdp2
172.16.25.141 hdp3
172.16.25.142 hdp4
172.16.25.143 hdp5

#关闭防火墙
systemctl stop firewalld
systemctl disable firewalld

2、配置免密码登陆

分别在hdp1、hdp2配置免密登陆

在hdp1上
cd ~
ssh-keygen -t rsa
cd .ssh
cat id_rsa.pub >> authorized_keys
scp ~/.ssh/authorized_keys hdp1:~/.ssh
scp ~/.ssh/authorized_keys hdp2:~/.ssh
scp ~/.ssh/authorized_keys hdp3:~/.ssh
scp ~/.ssh/authorized_keys hdp4:~/.ssh

在hdp2上
cd ~/.ssh
cat id_rsa.pub >> authorized_keys
scp ~/.ssh/authorized_keys hdp1:~/.ssh
scp ~/.ssh/authorized_keys hdp2:~/.ssh
scp ~/.ssh/authorized_keys hdp3:~/.ssh
scp ~/.ssh/authorized_keys hdp4:~/.ssh

3、配置JDK

每台主机分别配置

解压安装包
/opt/jdk1.8.0_131

配置环境变量，在文件/etc/profile末尾添加如下内容：
export JAVA_HOME=/opt/jdk1.8.0_131
export PATH=$PATH:$JAVA_HOME/bin

使环境变量生效
source /etc/profile

4、yum源配置、清理、缓存

配置yum源

rpm包放到下面的目录中
/var/www/html

配置hdp的repo
cat /etc/yum.repos.d/hdp.repo
[hdp]
name=hadoop
baseurl=http://172.16.25.139
enabled=1
gpgcheck=0

启动http服务
service httpd start

yum clean all
yum makecache

5、所有关于hadoop的rpm包

 yum list | grep hadoop
hadoop.x86_64                              3.1.1-1.el7                 @hdp
hadoop-hdfs.x86_64                         3.1.1-1.el7                 @hdp
hadoop-hdfs-journalnode.x86_64             3.1.1-1.el7                 @hdp
hadoop-hdfs-namenode.x86_64                3.1.1-1.el7                 @hdp
hadoop-hdfs-zkfc.x86_64                    3.1.1-1.el7                 @hdp
hadoop-hdfs-datanode.x86_64                3.1.1-1.el7                 hdp
hadoop-hdfs-fuse.x86_64                    3.1.1-1.el7                 hdp
hadoop-hdfs-secondarynamenode.x86_64       3.1.1-1.el7                 hdp
hadoop-libhdfs.x86_64                      3.1.1-1.el7                 hdp
hadoop-libhdfs-devel.x86_64                3.1.1-1.el7                 hdp


hadoop-mapreduce.x86_64                    3.1.1-1.el7                 @hdp
hadoop-mapreduce-historyserver.x86_64      3.1.1-1.el7                 @hdp

hadoop-yarn.x86_64                         3.1.1-1.el7                 @hdp
hadoop-yarn-nodemanager.x86_64             3.1.1-1.el7                 @hdp
hadoop-yarn-resourcemanager.x86_64         3.1.1-1.el7                 @hdp
hadoop-yarn-proxyserver.x86_64             3.1.1-1.el7                 hdp
hadoop-yarn-timelineserver.x86_64          3.1.1-1.el7                 hdp

hadoop-client.x86_64                       3.1.1-1.el7                 hdp
hadoop-debuginfo.x86_64                    3.1.1-1.el7                 hdp
hadoop-doc.x86_64                          3.1.1-1.el7                 hdp

hadoop-conf-pseudo.x86_64                  3.1.1-1.el7                 hdp

6、服务分配

hostname	ip	说明
hdp1	172.16.25.139	name node
hdp2	172.16.25.140	name node
hdp3	172.16.25.141	data node
hdp4	172.16.25.142	data node
hdp5	172.16.25.143	data node

	hdp1	hdp2	hdp3	hdp4	hdp5
NameNode	√	√
DataNode			√	√	√
ResourceManager	√	√
NodeManager	√	√	√	√	√
Zookeeper	√	√	√	√	√
journalnode	√	√	√	√	√
zkfc	√	√

7、安装服务

hdp1

yum install zookeeper  -y
yum install hadoop-hdfs-namenode -y
yum install hadoop-yarn-resourcemanager -y
yum install hadoop-hdfs-zkfc -y
yum install hadoop-yarn-nodemanager -y 
yum install hadoop-hdfs-journalnode -y
yum install hadoop-mapreduce-historyserver -y
yum install hadoop-yarn-proxyserver -y    
yum install hadoop-yarn-timelineserver -y
yum install hadoop-hdfs-secondarynamenode -y

hdp2

yum install zookeeper  -y
yum install hadoop-hdfs-namenode -y
yum install hadoop-yarn-resourcemanager -y
yum install hadoop-hdfs-zkfc -y 
yum install hadoop-yarn-nodemanager -y 
yum install hadoop-hdfs-journalnode -y
yum install hadoop-mapreduce-historyserver -y
yum install hadoop-hdfs-secondarynamenode -y

hdp3

yum install zookeeper  -y
yum install hadoop-hdfs-datanode -y
yum install hadoop-yarn-nodemanager -y
yum install hadoop-hdfs-journalnode -y
yum install hadoop-mapreduce-historyserver -y

hdp4和hdp5

yum install hadoop-hdfs-datanode -y
yum install hadoop-yarn-nodemanager -y
yum install hadoop-hdfs-journalnode -y
yum install hadoop-mapreduce-historyserver -y

8、配置服务

zookeeper

zoo.cfg添加

dataLogDir=/var/lib/zookeeper/log

server.1=hdp1:2888:3888
server.2=hdp2:2888:3888
server.3=hdp3:2888:3888
在data目录下添加myid文件，内容为对应每台主机的serverid，分别为1，2，3

mkdir /var/lib/zookeeper/log

hadoop

hadoop-env.sh

## 在文件开头加上，根据自己服务器配置设置jvm内存大小
export JAVA_HOME=/opt/jdk1.8.0_131
#export PATH=$PATH:$JAVA_HOME/bin
#export HADOOP_NAMENODE_OPTS=" -Xms1024m -Xmx1024m -XX:+UseParallelGC"
#export HADOOP_DATANODE_OPTS=" -Xms512m -Xmx512m"
export HADOOP_LOG_DIR=/opt/data/logs/hadoop

core-site.xml

<configuration>
    <!-- 指定hdfs的nameservice为mycluster -->
    <property>
        <name>fs.defaultFS</name>
        <value>hdfs://mycluster</value>
    </property>

    <property>
        <name>hadoop.tmp.dir</name>
        <value>/opt/data/hadoop/tmp</value>
    </property>

    <!-- 指定zookeeper地址 -->
<property>
    <name>ha.zookeeper.quorum</name>
    <value>hdp1:2181,hdp2:2181,hdp3:2181</value>
</property>

<!-- hadoop链接zookeeper的超时时长设置 -->
     <property>
         <name>ha.zookeeper.session-timeout.ms</name>
         <value>30000</value>
         <description>ms</description>
     </property>

    <property>
        <name>fs.trash.interval</name>
        <value>1440</value>
    </property>

</configuration>

hdfs-site.xml

<configuration>

<!-- journalnode集群之间通信的超时时间 -->
<property>
    <name>dfs.qjournal.start-segment.timeout.ms</name>
    <value>60000</value>
</property>
    <!--指定hdfs的nameservice为mycluster，需要和core-site.xml中的保持一致
                          dfs.ha.namenodes.[nameservice id]为在nameservice中的每一个NameNode设置唯一标示符。
        配置一个逗号分隔的NameNode ID列表。这将是被DataNode识别为所有的NameNode。
        例如，如果使用"mycluster"作为nameservice ID，并且使用"hdp1"和"hdp2"作为NameNodes标示符
    -->
    <property>
        <name>dfs.nameservices</name>
        <value>mycluster</value>
    </property>
        <!-- mycluster下面有两个NameNode，分别是hdp1，hdp2 -->
    <property>
        <name>dfs.ha.namenodes.mycluster</name>
        <value>nn1,nn1</value>
    </property>
    <!-- hdp1的RPC通信地址 -->
    <property>
        <name>dfs.namenode.rpc-address.mycluster.nn1</name>
        <value>hdp1:8020</value>
    </property>
    <!-- hdp2的RPC通信地址 -->
    <property>
        <name>dfs.namenode.rpc-address.mycluster.nn2</name>
        <value>hdp2:8020</value>
    </property>
     <!-- hdp1的http通信地址 -->
    <property>
        <name>dfs.namenode.http-address.mycluster.nn1</name>
        <value>hdp1:50070</value>
    </property>
    <!-- hdp2的http通信地址 -->
    <property>
        <name>dfs.namenode.http-address.mycluster.nn2</name>
        <value>hdp2:50070</value>
    </property>
    <!-- 指定NameNode的edits元数据的共享存储位置。也就是JournalNode列表,该url的配置格式：qjournal://host1:port1;host2:port2;host3:port3/journalId
        journalId推荐使用nameservice，默认端口号是：8485 -->
    <property>
        <name>dfs.namenode.shared.edits.dir</name>
       <value>qjournal://hdp1:8485;hdp2:8485;hdp3:8485;hdp4:8485;hdp5:8485/mycluster</value>
    </property>
    <!-- 配置失败自动切换实现方式 -->
    <property>
        <name>dfs.client.failover.proxy.provider.mycluster</name>
    <value>org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider</value>
    </property>
    <!-- 配置隔离机制方法，多个机制用换行分割，即每个机制暂用一行 -->
    <property>
        <name>dfs.ha.fencing.methods</name>
        <value>
            sshfence
	    shell(/bin/true)
        </value>
    </property>
  <property>
     <name>dfs.permissions.enabled</name>
     <value>false</value>
  </property>
    <property>
        <name>dfs.support.append</name>
        <value>true</value>
    </property>
    <!-- 使用sshfence隔离机制时需要ssh免登陆 -->
    <property>
        <name>dfs.ha.fencing.ssh.private-key-files</name>
        <value>/root/.ssh/id_rsa</value>
    </property>
    <!-- 指定副本数 -->
    <property>
        <name>dfs.replication</name>
        <value>2</value>
    </property>
    <property>
        <name>dfs.namenode.name.dir</name>
        <value>/opt/data/hadoop/hdfs/nn</value>
    </property>
    <property>
        <name>dfs.datanode.data.dir</name>
        <value>/opt/data/hadoop/hdfs/dn</value>
    </property>
    <!-- 指定JournalNode在本地磁盘存放数据的位置 -->
    <property>
        <name>dfs.journalnode.edits.dir</name>
        <value>/opt/data/hadoop/hdfs/jn</value>
    </property>
    <!-- 开启NameNode失败自动切换 -->
    <property>
        <name>dfs.ha.automatic-failover.enabled</name>
        <value>true</value>
    </property>
    <!-- 启用webhdfs -->
    <property>
        <name>dfs.webhdfs.enabled</name>
        <value>true</value>
    </property>
    <!-- 配置sshfence隔离机制超时时间 -->
    <property>
        <name>dfs.ha.fencing.ssh.connect-timeout</name>
        <value>30000</value>
    </property>
    <property>
        <name>ha.failover-controller.cli-check.rpc-timeout.ms</name>
        <value>60000</value>
    </property>

</configuration>

mapred-site.xml

<configuration>
    <!-- 指定mr框架为yarn方式 -->
    <property>
        <name>mapreduce.framework.name</name>
        <value>yarn</value>
    </property>

    <!-- 指定mapreduce jobhistory地址 -->
    <property>
        <name>mapreduce.jobhistory.address</name>
        <value>hdp1:10020</value>
    </property>
    <!-- 任务历史服务器的web地址 -->
    <property>
        <name>mapreduce.jobhistory.webapp.address</name>
        <value>hdp1:19888</value>
    </property>
    <property>
      <name>mapreduce.application.classpath</name>
      <value>
          /usr/lib/hadoop/etc/hadoop,
          /usr/lib/hadoop/*,
          /usr/lib/hadoop/lib/*,
          /usr/lib/hadoop-hdfs/*,
          /usr/lib/hadoop-hdfs/lib/*,
        /usr/lib/mapreduce/*,
         /usr/lib/mapreduce/lib/*,
          /usr/lib/hadoop-yarn/*,
          /usr/lib/hadoop-yarn/lib/*
      </value>
    </property>
</configuration>

yarn-site.xml

<configuration>

<!-- Site specific YARN configuration properties -->
    <!-- 开启RM高可用 -->
    <property>
        <name>yarn.resourcemanager.ha.enabled</name>
        <value>true</value>
    </property>
    <!-- 指定RM的cluster id -->
    <property>
        <name>yarn.resourcemanager.cluster-id</name>
        <value>yrc</value>
    </property>
    <!-- 指定RM的名字 -->
    <property>
        <name>yarn.resourcemanager.ha.rm-ids</name>
        <value>rm1,rm2</value>
    </property>
    <!-- 分别指定RM的地址 -->
    <property>
        <name>yarn.resourcemanager.hostname.rm1</name>
        <value>hdp1</value>
    </property>
    <property>
        <name>yarn.resourcemanager.hostname.rm2</name>
        <value>hdp2</value>
    </property>
    <!-- 指定zk集群地址 -->
    <property>
        <name>yarn.resourcemanager.zk-address</name>
        <value>hdp1:2181,hdp2:2181,hdp3:2181</value>
    </property>
    <property>
        <name>yarn.nodemanager.aux-services</name>
        <value>mapreduce_shuffle</value>
    </property>
    <property>
        <name>yarn.log-aggregation-enable</name>
        <value>true</value>
    </property>
    <property>
        <name>yarn.log-aggregation.retain-seconds</name>
        <value>86400</value>
    </property>
  <property>
  <name>yarn.resourcemanager.webapp.address.rm1</name>
  <value>hdp1:8088</value>
</property>
<property>
  <name>yarn.resourcemanager.webapp.address.rm2</name>
  <value>hdp2:8088</value>
</property>
    <!-- 启用自动恢复 -->
    <property>
        <name>yarn.resourcemanager.recovery.enabled</name>
        <value>true</value>
    </property>
    <!-- 制定resourcemanager的状态信息存储在zookeeper集群上 -->
    <property>
        <name>yarn.resourcemanager.store.class</name>
        <value>org.apache.hadoop.yarn.server.resourcemanager.recovery.ZKRMStateStore</value>
    </property>
</configuration>

workers

hdp3
hdp4
hdp5

9、创建目录

hdp1-hdp5

mkdir -p /opt/data/logs/hadoop
mkdir -p /opt/data/hadoop/hdfs/nn
mkdir -p /opt/data/hadoop/hdfs/dn
mkdir -p /opt/data/hadoop/hdfs/jn
mkdir -p /opt/data/hadoop/tmp
mkdir -p /opt/data/hadoop/tmp/yarn/timeline/
 
chown -R hdfs:hdfs /opt/data
chown -R yarn:yarn /opt/data/hadoop/tmp/yarn

export BIGTOP_DEFAULTS_DIR=/usr/lib
export HADOOP_PREFIX=/usr/lib/hadoop
export YARN_CONF_DIR=/usr/lib/hadoop-yarn

10、启动服务

zookeeper

hdp1-hdp3启动

hadoop

#启动journalnode
hdp1,hdp2,hdp3,hdp4,hdp5
/etc/init.d/hadoop-hdfs-journalnode start

#格式化namenode
hdp1
hadoop namenode -format


#格式化zkfc
hdp1
hdfs zkfc -formatZK
./hadoop-hdfs-zkfc start


#启动HDFS,zookeeper会选举哪个是active，哪个是standby
hdp1&hdp2
/etc/init.d/hadoop-hdfs-namenode start


#启动YARN
#在主备 resourcemanager 中随便选择一台进行启动
start-yarn.sh


#启动 mapreduce 任务历史服务器
mr-jobhistory-daemon.sh start historyserver