newMorning

单机伪集群部署

配置环境

vim /etc/hosts
```
172.16.30.134 hadoop1
```

# 配置本机免密
ssh-keygen
ssh-copy-id hadoop1

下载jdk和hadoop安装包

wget https://dlcdn.apache.org/hadoop/common/hadoop-3.4.0/hadoop-3.4.0.tar.gz
wget https://javadl.oracle.com/webapps/download/AutoDL?BundleId=244045_89d678f2be164786b292527658ca1605

安装

mv jdk1.8.0_281/ /opt/jdk
mv hadoop-3.4.0/ /opt/hadoop

cat ~/.bash_profile
```
export JAVA_HOME=/opt/jdk
export PATH=$JAVA_HOME/bin:$PATH
export HADOOP_HOME=/opt/hadoop
export PATH=$HADOOP_HOME/bin:$HADOOP_HOME/sbin:$PATH
```

source ~/.bash_profile

java -version
hadoop version

vim /opt/hadoop/etc/hadoop/hadoop-env.sh
```
export JAVA_HOME=/opt/jdk
```

mkdir -p /data/hadoop/tmp
mkdir -p /data/hadoop/hdfs/name
mkdir -p /data/hadoop/hdfs/d
mkdir -p /data/hadoop/log

cd /opt/hadoop/etc/hadoop/
vim core-site.xml
<configuration>
    <property>
        <name>fs.defaultFS</name>
        <value>hdfs://hadoop1:9000</value>
    </property>
    <property>
        <name>hadoop.tmp.dir</name>
        <value>file:/data/hadoop/tmp</value>
    </property>
</configuration>


vim hdfs-site.xml
<configuration>
  <property>
    <name>dfs.namenode.http-address</name>
      <value>hadoop1:50070</value>
  </property>
  <property>
    <name>dfs.namenode.secondary.http-address</name>
      <value>hadoop1:50090</value>
  </property>
    <property>
        <name>dfs.namenode.name.dir</name>
        <value>file:/data/hadoop/hdfs/name</value>
    </property>
    <property>
        <name>dfs.datanode.data.dir</name>
        <value>file:/data/hadoop/hdfs/data</value>
    </property>
    <property>
        <name>dfs.replication</name>
        <value>1</value>
    </property>
</configuration>

启动Hadoop

# 首次启动初始化
hdfs namenode -format

# root用户启动
export HDFS_NAMENODE_USER="root"
export HDFS_DATANODE_USER="root"
export HDFS_SECONDARYNAMENODE_USER="root"

# 启动hdfs
start-dfs.sh

# 查看进程
jps

# 查看hdfs报告
hdfs dfsadmin -report

MapReduce测试

略

HDFS测试

hdfs dfs -mkdir -p /user/root
hdfs dfs -mkdir input

# 上传文件到input目录
hadoop fs -put ./etc/hadoop/*.xml input

# 列出目录
hadoop fs -ls input

# 取出文件到本地
hadoop fs -get /user/root/input/core-site.xml .

# 远端取出文件
hadoop fs -get hdfs://hadoop1:9000/user/root/input/core-site.xml

Web文件浏览

配置NFS Gateway

vim core-site.xml
```
    <property>
      <name>hadoop.proxyuser.root.groups</name>
      <value>*</value>
      <description>
             The 'nfsserver' user is allowed to proxy all members of the 'users-group1' and
             'users-group2' groups. Note that in most cases you will need to include the
             group "root" because the user "root" (which usually belonges to "root" group) will
             generally be the user that initially executes the mount on the NFS client system.
             Set this to '*' to allow nfsserver user to proxy any group.
      </description>
    </property>

    <property>
      <name>hadoop.proxyuser.root.hosts</name>
      <value>*</value>
      <description>
             This is the host where the nfs gateway is running. Set this to '*' to allow
             requests from any hosts to be proxied.
      </description>
    </property>
```

vim hdfs-site.xml
```
<property>
    <name>nfs.superuser</name>
    <value>root</value>
</property>
 <property>
    <name>dfs.namenode.accesstime.precision</name>
    <value>3600000</value>
    <description>The access time for HDFS file is precise upto this value.
           The default value is 1 hour. Setting a value of 0 disables
           access times for HDFS.
     </description>
</property>
<property>
     <name>nfs.dump.dir</name>
     <value>/data/hadoop/tmp/.hdfs-nfs</value>
</property>
<property>
     <name>nfs.exports.allowed.hosts</name>
     <value>* rw</value>
</property>
```

vim log4j.properties
```
log4j.logger.org.apache.hadoop.hdfs.nfs=DEBUG
log4j.logger.org.apache.hadoop.oncrpc=DEBUG
```

# 重启hdfs
stop-dfs.sh
start-dfs.sh
hadoop-daemon.sh start portmap
hadoop-daemon.sh start nfs3

rpcinfo -p 172.16.30.134

# 客户端挂载
mount -t nfs 172.16.30.134:/ /mnt/hadoop/

附：容量速算

容量计算

存储容量 = 数据块大小 * 数据块数量 * 副本数量

内存计算

# 1GB内存可以管理百万个block文件
# 400TB数据，4节点集群，双副本
数据块数量 = 数据量 / (块大小 * 副本数量)