[大数据]阿里云hadoop集群环境搭建

1、基础环境准备

1.1、yum安装rz、sz命令(上传下载)

yum -y install lrzsz

1.2、集群分发脚本xsync

#!/bin/bash
#1. 判断参数个数
if [ $# -lt 1 ]
then
  echo Not Enough Arguement!
  exit;
fi
#2. 遍历集群所有机器
for host in h102 h103 h104
do
  echo ====================  $host  ====================
  #3. 遍历所有目录，挨个发送
  for file in $@
  do
    #4 判断文件是否存在
    if [ -e $file ]
    then
      #5. 获取父目录
      pdir=$(cd -P $(dirname $file); pwd)
      #6. 获取当前文件的名称
      fname=$(basename $file)
      ssh $host "mkdir -p $pdir"
      rsync -av $pdir/$fname $host:$pdir
    else
      echo $file does not exists!
    fi
  done
done

1.3、集群日志生成脚本lg.sh

#!/bin/bash
for i in h102 h103; do
    echo "========== $i =========="
    ssh $i "cd /opt/module/applog/; java -jar gmall2020-mock-log-2021-01-22.jar >/dev/null 2>&1 &"
done 

1.4、多个服务器同步执行shell命令脚本

#! /bin/bash
 
for i in h102 h103 h104
do
    echo --------- $i ----------
    ssh $i "$*"
done

1.5、ssh无密登录配置

h102上生成公钥和私钥

ssh-keygen -t rsa

将h102公钥拷贝到要免密登录的目标机器上

ssh-copy-id h102
ssh-copy-id h103
ssh-copy-id h104

h103和h104重复同样的操作

1.6、jdk准备

卸载现有的jdk

sudo rpm -qa | grep -i java | xargs -n1 sudo rpm -e --nodeps

将jdk安装包上传到服务器/opt/software/目录下，使用tar -zxvf解压

tar -zxvf jdk-8u212-linux-x64.tar.gz -C /opt/module/

创建环境变量配置文件

vim /etc/profile.d/my_env.sh

让环境变量生效

source /etc/profile.d/my_env.sh

测试jdk是否安装成功

java -version

分发jdk

xsync /opt/module/jdk1.8.0_212/

分发环境变量配置文件

xsync /etc/profile.d/my_env.sh

在每台服务器上分别执行

source /etc/profile.d/my_env.sh

1.7、环境变量配置说明

Linux的环境变量可在多个文件中配置，如/etc/profile，/etc/profile.d/*.sh，_/.bashrc，/.bash_profile等，下面说明上述几个文件之间的关系和区别。

bash的运行模式可分为login shell和non-login shell。

例如，我们通过终端，输入用户名、密码，登录系统之后，得到就是一个login shell。而当我们执行以下命令ssh hadoop103 command，在hadoop103执行command的就是一个non-login shell。

这两种shell的主要区别在于，它们启动时会加载不同的配置文件，login shell启动时会加载/etc/profile，_{/.bash_profile，}/.bashrc。non-login shell启动时会加载~/.bashrc。

而在加载_{/.bashrc（实际是}/.bashrc中加载的/etc/bashrc）或/etc/profile时，都会执行如下代码片段，

	for i in /etc/profile.d/*.sh; do
        if [ -r "$i" ]; then
            if [ "$PS1" ]; then
                . "$i"
            else
                . "$i" >/dev/null
            fi
        fi
    done

因此不管是login shell还是non-login shell，启动时都会加载/etc/profile.d/*.sh中的环境变量。

2、hadoop

2.1、hadoop安装配置

上传hadoop安装包到/opt/software/目录下，并解压到/opt/module/

tar -zxvf hadoop-3.1.3.tar.gz -C /opt/module/

在/etc/profile.d/my_env.sh中添加hadoop环境变量

#HADOOP_HOME
export HADOOP_HOME=/opt/module/hadoop-3.1.3
export PATH=$PATH:$HADOOP_HOME/bin
export PATH=$PATH:$HADOOP_HOME/sbin

分发hadoop到所有服务器

xsync hadoop-3.1.3/

分发环境变量到所有服务器

xsync /etc/profile.d/my_env.sh

在所有服务器使环境变量生效

source /etc/profile.d/my_env.sh

2.2、配置集群

集群规划

配置core-site.xml

<?xml version="1.0" encoding="UTF-8"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>

<configuration>
	<!-- 指定NameNode的地址 -->
    <property>
        <name>fs.defaultFS</name>
        <value>hdfs://h102:8020</value>
    </property>
    <!-- 指定hadoop数据的存储目录 -->
        <property>
            <name>hadoop.tmp.dir</name>
            <value>/opt/module/hadoop-3.1.3/data</value>
    </property>

    <!-- 配置HDFS网页登录使用的静态用户为atguigu -->
        <property>
            <name>hadoop.http.staticuser.user</name>
            <value>root</value>
    </property>

    <!-- 配置该atguigu(superUser)允许通过代理访问的主机节点 -->
        <property>
            <name>hadoop.proxyuser.atguigu.hosts</name>
            <value>*</value>
    </property>
    <!-- 配置该atguigu(superUser)允许通过代理用户所属组 -->
        <property>
            <name>hadoop.proxyuser.atguigu.groups</name>
            <value>*</value>
    </property>
    <!-- 配置该atguigu(superUser)允许通过代理的用户-->
        <property>
            <name>hadoop.proxyuser.atguigu.users</name>
            <value>*</value>
    </property>
</configuration>

配置hdfs-site.xml

<?xml version="1.0" encoding="UTF-8"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>

<configuration>
	<!-- nn web端访问地址-->
	<property>
        <name>dfs.namenode.http-address</name>
        <value>h102:9870</value>
    </property>
    
	<!-- 2nn web端访问地址-->
    <property>
        <name>dfs.namenode.secondary.http-address</name>
        <value>h104:9868</value>
    </property>
    
    <!-- 测试环境指定HDFS副本的数量1 -->
    <property>
        <name>dfs.replication</name>
        <value>1</value>
    </property>
</configuration>

配置yarn-site.xml

<?xml version="1.0" encoding="UTF-8"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>

<configuration>
	<!-- 指定MR走shuffle -->
    <property>
        <name>yarn.nodemanager.aux-services</name>
        <value>mapreduce_shuffle</value>
    </property>
    
    <!-- 指定ResourceManager的地址-->
    <property>
        <name>yarn.resourcemanager.hostname</name>
        <value>h103</value>
    </property>
    
    <!-- 环境变量的继承 -->
    <property>
        <name>yarn.nodemanager.env-whitelist</name>
        <value>JAVA_HOME,HADOOP_COMMON_HOME,HADOOP_HDFS_HOME,HADOOP_CONF_DIR,CLASSPATH_PREPEND_DISTCACHE,HADOOP_YARN_HOME,HADOOP_MAPRED_HOME</value>
    </property>
    
    <!-- yarn容器允许分配的最大最小内存 -->
    <property>
        <name>yarn.scheduler.minimum-allocation-mb</name>
        <value>512</value>
    </property>
    <property>
        <name>yarn.scheduler.maximum-allocation-mb</name>
        <value>4096</value>
    </property>
    
    <!-- yarn容器允许管理的物理内存大小 -->
    <property>
        <name>yarn.nodemanager.resource.memory-mb</name>
        <value>4096</value>
    </property>
    
    <!-- 关闭yarn对虚拟内存的限制检查 -->
    <property>
        <name>yarn.nodemanager.vmem-check-enabled</name>
        <value>false</value>
    </property>
</configuration>

配置mapred-site.xml

<?xml version="1.0" encoding="UTF-8"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>

<configuration>
	<!-- 指定MapReduce程序运行在Yarn上 -->
    <property>
        <name>mapreduce.framework.name</name>
        <value>yarn</value>
    </property>
</configuration>

配置workers

h102
h103
h104

注意：workers文件内容后面会读取作为主机名使用，所以不可以有空行和空格，否则会报错。

2.3、配置历史服务器

为了查看程序的历史运行情况，需要配置一下历史服务器。

在mapred-site.xml文件里面增加如下配置：

<!-- 历史服务器端地址 -->
<property>
    <name>mapreduce.jobhistory.address</name>
    <value>h102:10020</value>
</property>

<!-- 历史服务器web端地址 -->
<property>
    <name>mapreduce.jobhistory.webapp.address</name>
    <value>h102:19888</value>
</property>

2.4、配置日志的聚集

在yarn-site.xml文件里面增加如下配置：

<!-- 开启日志聚集功能 -->
<property>
    <name>yarn.log-aggregation-enable</name>
    <value>true</value>
</property>

<!-- 设置日志聚集服务器地址 -->
<property>  
    <name>yarn.log.server.url</name>  
    <value>http://h102:19888/jobhistory/logs</value>
</property>

<!-- 设置日志保留时间为7天 -->
<property>
    <name>yarn.log-aggregation.retain-seconds</name>
    <value>604800</value>
</property>

2.5、启动集群

（1）如果集群是第一次启动，需要在hadoop102节点格式化NameNode（注意格式化之前，一定要先停止上次启动的所有namenode和datanode进程，然后再删除data和log数据）

[root@h102 hadoop-3.1.3]$ bin/hdfs namenode -format

（2）启动HDFS

[root@h102 hadoop-3.1.3]$ sbin/start-dfs.sh

root用户启动报错：

Starting namenodes on [h102]
ERROR: Attempting to operate on hdfs namenode as root
ERROR: but there is no HDFS_NAMENODE_USER defined. Aborting operation.
Starting datanodes
ERROR: Attempting to operate on hdfs datanode as root
ERROR: but there is no HDFS_DATANODE_USER defined. Aborting operation.
Starting secondary namenodes [h104]
ERROR: Attempting to operate on hdfs secondarynamenode as root
ERROR: but there is no HDFS_SECONDARYNAMENODE_USER defined. Aborting operation.

网上查询了一下，大概就是我们用root用户操作，没有在环境变量中定义变量。

export HDFS_NAMENODE_USER=root
export HDFS_DATANODE_USER=root
export HDFS_SECONDARYNAMENODE_USER=root
export YARN_RESOURCEMANAGER_USER=root
export YARN_NODEMANAGER_USER=root

再次运行sbin/start-dfs.sh，启动成功。

（3）在配置了ResourceManager的节点（h103）启动YARN

[root@h103 hadoop-3.1.3]$ sbin/start-yarn.sh

（4）Web端查看HDFS的Web页面：http://h102:9870/

如果连接不上，检查本机hosts文件是否配置，阿里云安全组规则中是否添加了9870端口。

2.6、hadoop集群启停脚本

在/root/bin目录下创建脚本hdp.sh

#!/bin/bash
if [ $# -lt 1 ]
then
    echo "No Args Input..."
    exit ;
fi
case $1 in
"start")
        echo " =================== 启动 hadoop集群 ==================="

        echo " --------------- 启动 hdfs ---------------"
        ssh h102 "/opt/module/hadoop-3.1.3/sbin/start-dfs.sh"
        echo " --------------- 启动 yarn ---------------"
        ssh h103 "/opt/module/hadoop-3.1.3/sbin/start-yarn.sh"
        echo " --------------- 启动 historyserver ---------------"
        ssh h102 "/opt/module/hadoop-3.1.3/bin/mapred --daemon start historyserver"
;;
"stop")
        echo " =================== 关闭 hadoop集群 ==================="

        echo " --------------- 关闭 historyserver ---------------"
        ssh h102 "/opt/module/hadoop-3.1.3/bin/mapred --daemon stop historyserver"
        echo " --------------- 关闭 yarn ---------------"
        ssh h103 "/opt/module/hadoop-3.1.3/sbin/stop-yarn.sh"
        echo " --------------- 关闭 hdfs ---------------"
        ssh h102 "/opt/module/hadoop-3.1.3/sbin/stop-dfs.sh"
;;
*)
    echo "Input Args Error..."
;;
esac

2.7、支持LZO压缩配置

将编译好后的hadoop-lzo-0.4.20.jar 放入hadoop-3.1.3/share/hadoop/common/，然后同步hadoop-lzo-0.4.20.jar到h103、h104

core-site.xml增加配置支持LZO压缩：

<configuration>
    <property>
        <name>io.compression.codecs</name>
        <value>
            org.apache.hadoop.io.compress.GzipCodec,
            org.apache.hadoop.io.compress.DefaultCodec,
            org.apache.hadoop.io.compress.BZip2Codec,
            org.apache.hadoop.io.compress.SnappyCodec,
            com.hadoop.compression.lzo.LzoCodec,
            com.hadoop.compression.lzo.LzopCodec
        </value>
    </property>

    <property>
        <name>io.compression.codec.lzo.class</name>
        <value>com.hadoop.compression.lzo.LzoCodec</value>
    </property>
</configuration>

准备测试数据：

hadoop fs -mkdir /input
hadoop fs -put README.txt /input

测试压缩：

hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.3.jar wordcount -Dmapreduce.output.fileoutputformat.compress=true -Dmapreduce.output.fileoutputformat.compress.codec=com.hadoop.compression.lzo.LzopCodec  /input /output

2.8、LZO文件创建索引

创建索引

hadoop jar /opt/module/hadoop-3.1.3/share/hadoop/common/hadoop-lzo-0.4.20.jar  com.hadoop.compression.lzo.DistributedLzoIndexer /input/bigtable.lzo

再次执行WordCount程序

hadoop jar /opt/module/hadoop-3.1.3/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.3.jar wordcount -Dmapreduce.job.inputformat.class=com.hadoop.mapreduce.LzoTextInputFormat /input /output2

发现输出的切片数变成了2

2022-04-05 18:28:46,353 INFO client.RMProxy: Connecting to ResourceManager at h103/172.22.32.218:8032
2022-04-05 18:28:46,898 INFO mapreduce.JobResourceUploader: Disabling Erasure Coding for path: /tmp/hadoop-yarn/staging/root/.staging/job_1649154366974_0002
2022-04-05 18:28:47,011 INFO sasl.SaslDataTransferClient: SASL encryption trust check: localHostTrusted = false, remoteHostTrusted = false
2022-04-05 18:28:47,127 INFO input.FileInputFormat: Total input files to process : 2
2022-04-05 18:28:47,155 INFO sasl.SaslDataTransferClient: SASL encryption trust check: localHostTrusted = false, remoteHostTrusted = false
2022-04-05 18:28:47,220 INFO sasl.SaslDataTransferClient: SASL encryption trust check: localHostTrusted = false, remoteHostTrusted = false
2022-04-05 18:28:47,267 INFO sasl.SaslDataTransferClient: SASL encryption trust check: localHostTrusted = false, remoteHostTrusted = false
2022-04-05 18:28:47,274 INFO mapreduce.JobSubmitter: number of splits:2
2022-04-05 18:28:47,417 INFO sasl.SaslDataTransferClient: SASL encryption trust check: localHostTrusted = false, remoteHostTrusted = false
2022-04-05 18:28:47,838 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1649154366974_0002
2022-04-05 18:28:47,839 INFO mapreduce.JobSubmitter: Executing with tokens: []
2022-04-05 18:28:48,003 INFO conf.Configuration: resource-types.xml not found
2022-04-05 18:28:48,004 INFO resource.ResourceUtils: Unable to find 'resource-types.xml'.
2022-04-05 18:28:48,060 INFO impl.YarnClientImpl: Submitted application application_1649154366974_0002
2022-04-05 18:28:48,106 INFO mapreduce.Job: The url to track the job: http://h103:8088/proxy/application_1649154366974_0002/
2022-04-05 18:28:48,107 INFO mapreduce.Job: Running job: job_1649154366974_0002
2022-04-05 18:28:59,328 INFO mapreduce.Job: Job job_1649154366974_0002 running in uber mode : false
2022-04-05 18:28:59,329 INFO mapreduce.Job:  map 0% reduce 0%
2022-04-05 18:29:17,147 INFO mapreduce.Job:  map 33% reduce 0%
2022-04-05 18:29:23,402 INFO mapreduce.Job:  map 54% reduce 0%
2022-04-05 18:29:27,730 INFO mapreduce.Job:  map 72% reduce 0%
2022-04-05 18:29:28,736 INFO mapreduce.Job:  map 81% reduce 0%
2022-04-05 18:29:34,883 INFO mapreduce.Job:  map 83% reduce 0%
2022-04-05 18:29:36,899 INFO mapreduce.Job:  map 100% reduce 0%
2022-04-05 18:29:41,932 INFO mapreduce.Job:  map 100% reduce 100%
2022-04-05 18:29:42,948 INFO mapreduce.Job: Job job_1649154366974_0002 completed successfully
2022-04-05 18:29:43,054 INFO mapreduce.Job: Counters: 53