centos7二进制安装Hadoop3

一、Hadoop 简介

1.1 Hadoop3核心组件

HDFS：分布式文件系统：解决海量数据存储

YARN：集群资源管理和任务调度框架：解决资源任务调度

MapReduce：分布式计算框架：解决海量数据计算

1.2 Hadoop集群简介　

Hadoop集群包括两个集群：HDFS  YARN
两个集群 逻辑上分离(互不影响、互不依赖)  物理上一起(部分进程在一台服务器上)
默认为主从架构

1.2.1 HDFS

主角色：NameNode(NN)
从角色：DataNode(DN)
主角色辅助角色：SecondrayNameNode(SNN)

1.2.2 YARN

主角色：ResourceManager(RM)
从角色：NodeManager(NM)

二、环境信息及准备

2.1 机器及机器角色规划

2.2 节点添加hosts解析

192.168.1.131 hdp01.dialev.com
192.168.1.132 hdp02.dialev.com
192.168.1.133 hdp03.dialev.com

2.3 关闭防火墙　　

2.4 hdp01到三台机器免密

echo "StrictHostKeyChecking no" >~/.ssh/config
ssh-copy-id -i 192.168.1.13{1..3}

2.5 时间同步

yum -y install ntpdate 
ntpdate ntp.aliyun.com 
echo '*/5 * * * * ntpdate ntp.aliyun.com 2>&1'  >> /var/spool/cron/root

2.6 调大用户文件描述符

vim /etc/security/limits.conf
* soft nofile 65535
* hard nofile 65535
 
# 配置需要重启才能生效

2.7 安装Java环境

tar xf jdk-8u65-linux-x64.tar.gz -C /usr/local/
cd /usr/local/
ln -sv jdk1.8.0_65/  java
 
vim /etc/profile.d/java.sh
export JAVA_HOME=/usr/local/java
export CLASSPATH=$JAVA_HOME/lib/tools.jar
export PATH=$JAVA_HOME/bin:$PATH
 
source /etc/profile.d/java.sh
java -version

三、安装Hadoop

3.1 解压安装包

此篇文档及Hadoop相关文档相关软件包统一在此百度网盘：

链接：https://pan.baidu.com/s/11F4THdIfgrULMn2gNcObRA?pwd=cjll

# https://archive.apache.org/dist/hadoop/common/ 也可以根据实际部署版本下载
tar xf hadoop-3.1.4.tar.gz -C /usr/local/
cd /usr/local/
ln -sv hadoop-3.1.4 hadoop

目录结构：
├── bin  Hadoop最基本的管理脚本和使用脚本的目录。
├── etc  Hadoop配置文件目录
├── include  编程库头文件，用于C++程序访问HDFS或者编写MapReduce
├── lib  Hadoop对外提供的编程动态和静态库
├── libexec  各个服务对外用的shell配置文件目录，可用于日志输出，启动参数等基本信息
├── sbin Hadoop管理脚本所在目录，主要保护HDFS和YARN中各类服务的启动/关闭脚本
└── share  Hadoop各个模块编译后的jar包目录，官方自带示例

3.2 修改Hadoop环境配置变量

参考：https://hadoop.apache.org/docs/r3.1.4/ #Configuration 章节，左侧最下方

cd etc/hadoop
cp hadoop-env.sh hadoop-env.sh-bak
vim hadoop-env.sh
export JAVA_HOME=/usr/local/java
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
export HADOOP_HOME=/usr/local/hadoop
export PATH=$PATH:$HADOOP_HOME/bin:$HADOOP_HOME/sbin

3.3 指定集群默认配置

cp core-site.xml  core-site.xml-bak
vim core-site.xml
<configuration>
    <!-- 指定HDFS老大（namenode）的通信地址 -->
    <property>
        <name>fs.defaultFS</name>
        <value>hdfs://hdp01.dialev.com:8020</value>
    </property>
    <!-- 指定hadoop运行时产生文件的存储路径 -->
    <property>
        <name>hadoop.tmp.dir</name>
        <value>/Hadoop/tmp</value>
    </property>
    <!-- 设置HDFS web UI访问用户 -->
    <property>
        <name>hadoop.http.staticuser.user</name>
        <value>root</value>
    </property>
</configuration>

3.4 修改SNN配置　　

cp hdfs-site.xml hdfs-site.xml-bak
vim hdfs-site.xml
<configuration>
    <!-- 设置namenode的http通讯地址 -->
    <property>
       <name>dfs.namenode.http-address</name>
       <value>hdp01.dialev.com:50070</value>
    </property>
    <!-- 设置secondarynamenode的http通讯地址 -->
    <property>
       <name>dfs.namenode.secondary.http-address</name>
       <value>hdp02.dialev.com:50090</value>
    </property>
     <!-- 设置namenode存放的路径 -->
    <property>
       <name>dfs.namenode.name.dir</name>
       <value>/Hadoop/name</value>
    </property>
    <!-- 设置hdfs副本数量 -->
    <property>
       <name>dfs.replication</name>
       <value>2</value>
    </property>
    <!-- 设置datanode存放的路径,如果不指定则使用hadoop.tmp.dir所指路径 -->
    <property>
        <name>dfs.datanode.data.dir</name>
        <value>/Hadoop/data</value>
        </property>
</configuration>

3.5 MapReduce配置

cp mapred-site.xml mapred-site.xml-bak
vim mapred-site.xml
<configuration>
    <!-- 通知框架MR使用YARN -->
    <property>
       <name>mapreduce.framework.name</name>
       <value>yarn</value>
    </property>
    <!-- MR App Mater 环境变量 -->
    <property>
      <name>yarn.app.mapreduce.am.env</name>
      <value>HADOOP_MAPRED_HOME=${HADOOP_HOME}</value>
    </property>
    
    <!-- MR Map Task 环境变量 -->
    <property>
      <name>mapreduce.map.env</name>
      <value>HADOOP_MAPRED_HOME=${HADOOP_HOME}</value>
    </property>
    
    <!-- MR Reduce Task 环境变量 -->
    <property>
      <name>mapreduce.reduce.env</name>
      <value>HADOOP_MAPRED_HOME=${HADOOP_HOME}</value>
    </property>
</configuration>

3.6 YARN配置

cp yarn-site.xml yarn-site.xml-bak
vim yarn-site.xml
<configuration>
<property>
    <name>yarn.resourcemanager.hostname</name>
    <value>hdp01.dialev.com</value>
</property>

<property>
    <name>yarn.nodemanager.aux-services</name>
    <value>mapreduce_shuffle</value>
</property>

<!-- 是否将对容器实施物理内存限制 -->
<property>
    <name>yarn.nodemanager.pmem-check-enabled</name>
    <value>false</value>
</property>

<!-- 是否将对容器实施虚拟内存限制。 -->
<property>
    <name>yarn.nodemanager.vmem-check-enabled</name>
    <value>false</value>
</property>

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

<!-- 保存的时间7天 -->
<property>
  <name>yarn.log-aggregation.retain-seconds</name>
  <value>604800</value>
</property>
</configuration>

3.7 配置从角色机器地址　　

vim workers
hdp01.dialev.com
hdp02.dialev.com
hdp03.dialev.com

8.同步集群配置

scp -r -q hadoop-3.1.4 192.168.1.132:/usr/local/
scp -r -q hadoop-3.1.4 192.168.1.133:/usr/local/

# 2 3 两个节点创建软连接
ln -sv hadoop-3.1.4 hadoop

四、启动Hadoop

4.1 初始化名称节点

在hdp01.dialev.com上执行,仅此一次，误操作可以删除初始化目录　

hdfs namenode -format
......
2022-12-26 16:40:03,355 INFO util.GSet: 0.029999999329447746% max memory 940.5 MB = 288.9 KB
2022-12-26 16:40:03,355 INFO util.GSet: capacity      = 2^15 = 32768 entries
2022-12-26 16:40:03,406 INFO namenode.FSImage: Allocated new BlockPoolId: BP-631728325-192.168.1.131-1672044003397
2022-12-26 16:40:03,437 INFO common.Storage: Storage directory /Hadoop/name has been successfully formatted.         # /Hadoop/name初始化目录，这行信息表明对应的存储已经格式化成功。
2022-12-26 16:40:03,498 INFO namenode.FSImageFormatProtobuf: Saving image file /Hadoop/name/current/fsimage.ckpt_0000000000000000000 using no compression
2022-12-26 16:40:03,781 INFO namenode.FSImageFormatProtobuf: Image file /Hadoop/name/current/fsimage.ckpt_0000000000000000000 of size 391 bytes saved in 0 seconds .
2022-12-26 16:40:03,802 INFO namenode.NNStorageRetentionManager: Going to retain 1 images with txid >= 0
2022-12-26 16:40:03,820 INFO namenode.FSImage: FSImageSaver clean checkpoint: txid = 0 when meet shutdown.
2022-12-26 16:40:03,821 INFO namenode.NameNode: SHUTDOWN_MSG: 
/************************************************************
SHUTDOWN_MSG: Shutting down NameNode at hdp01.dialev.com/192.168.1.131
************************************************************/

4.2 启动服务

1.自带sh脚本
cd hadoop/sbin/
HDFS集群:
    start-dfs.sh
    stop-dfs.sh
YARN集群:
    start-yarn.sh
    stop-yarn.sh
Hadoop集群(HDFS+YARN):
	start-all.sh
	stop-all.sh
jps  #查看命令结果是否与集群规划一致
/usr/local/hadoop/logs 日志路径，默认安装目录下logs目录





2.手动操作(了解)：
HDFS集群
hdfs --daemon start namenode | datanode |secondarynamenode
hdfs --daemon stop namenode | datanode | secondarynamenode
YARN集群
yarn --daemon start resourcemanager | nodemanager
yarn --daemon stop resourcemanager |nodemanager

五、验证

5.1 访问相关web UI

1.打印集群状态
hdfs dfsadmin -report

2.访问YARN的管理界面
http://192.168.1.131:8088/cluster/nodes   #RM服务所在主机8088端口

3.访问namenode的管理页面
http://192.168.1.131:50070/dfshealth.html#tab-overview  #NN服务所在主机，配置项为  hdfs-site.xml配置文件中dfs.namenode.http-address的值  2.x端口默认为50070  3.x默认为9870 这里50070是因为我没注意新版本改变还是写的2.x配置

5.2 测试创建、上传功能　　

hadoop fs -mkdir /bowen   #在Hadoop根目录下创建一个 bowen 目录
hadoop fs -put yarn-env.sh /bowen  #上传文件到bowen目录下

5.3 测试MapReduce执行

cd /usr/local/hadoop/share/hadoop/mapreduce
hadoop jar hadoop-mapreduce-examples-3.1.4.jar pi 2 4
Number of Maps  = 2
Samples per Map = 4
Wrote input for Map #0
Wrote input for Map #1
Starting Job
2022-12-27 09:20:12,868 INFO client.RMProxy: Connecting to ResourceManager at hdp01.dialev.com/192.168.1.131:8032  #首先会连接到RM上申请资源
2022-12-27 09:20:14,091 INFO mapreduce.JobResourceUploader: Disabling Erasure Coding for path: /tmp/hadoop-yarn/staging/root/.staging/job_1672045511416_0001
2022-12-27 09:20:14,503 INFO input.FileInputFormat: Total input files to process : 2
2022-12-27 09:20:14,707 INFO mapreduce.JobSubmitter: number of splits:2
2022-12-27 09:20:15,349 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1672045511416_0001
2022-12-27 09:20:15,351 INFO mapreduce.JobSubmitter: Executing with tokens: []
2022-12-27 09:20:16,072 INFO conf.Configuration: resource-types.xml not found
2022-12-27 09:20:16,073 INFO resource.ResourceUtils: Unable to find 'resource-types.xml'.
2022-12-27 09:20:16,974 INFO impl.YarnClientImpl: Submitted application application_1672045511416_0001
2022-12-27 09:20:17,204 INFO mapreduce.Job: The url to track the job: http://hdp01.dialev.com:8088/proxy/application_1672045511416_0001/
2022-12-27 09:20:17,206 INFO mapreduce.Job: Running job: job_1672045511416_0001
2022-12-27 09:20:33,618 INFO mapreduce.Job: Job job_1672045511416_0001 running in uber mode : false
2022-12-27 09:20:33,621 INFO mapreduce.Job:  map 0% reduce 0%    #MapReduce有两个阶段，分别是 map和reduce 
2022-12-27 09:20:47,862 INFO mapreduce.Job:  map 100% reduce 0%
2022-12-27 09:20:53,944 INFO mapreduce.Job:  map 100% reduce 100%
2022-12-27 09:20:53,968 INFO mapreduce.Job: Job job_1672045511416_0001 completed successfully
......

5.4 集群基准测试

 1.写测试:写入10个文件，每个10MB
hadoop jar hadoop-mapreduce-client-jobclient-3.1.4-tests.jar TestDFSIO -write -nrFiles 5 -fileSize 10MB
......
2022-12-27 09:51:12,775 INFO fs.TestDFSIO: ----- TestDFSIO ----- : write
2022-12-27 09:51:12,775 INFO fs.TestDFSIO:             Date & time: Tue Dec 27 09:51:12 CST 2022
2022-12-27 09:51:12,775 INFO fs.TestDFSIO:         Number of files: 5      #文件个数
2022-12-27 09:51:12,775 INFO fs.TestDFSIO:  Total MBytes processed: 50     #总大小
2022-12-27 09:51:12,775 INFO fs.TestDFSIO:       Throughput mb/sec: 12.49  #吞吐量
2022-12-27 09:51:12,775 INFO fs.TestDFSIO:  Average IO rate mb/sec: 15.46  #平均IO速率
2022-12-27 09:51:12,775 INFO fs.TestDFSIO:   IO rate std deviation: 7.51   #IO速率标准偏差
2022-12-27 09:51:12,776 INFO fs.TestDFSIO:      Test exec time sec: 32.95  #执行时间

2.读测试
hadoop jar hadoop-mapreduce-client-jobclient-3.1.4-tests.jar TestDFSIO -read -nrFiles 5 -fileSize 10MB
......
2022-12-27 09:54:23,826 INFO fs.TestDFSIO: ----- TestDFSIO ----- : read
2022-12-27 09:54:23,826 INFO fs.TestDFSIO:             Date & time: Tue Dec 27 09:54:23 CST 2022
2022-12-27 09:54:23,826 INFO fs.TestDFSIO:         Number of files: 5
2022-12-27 09:54:23,826 INFO fs.TestDFSIO:  Total MBytes processed: 50
2022-12-27 09:54:23,826 INFO fs.TestDFSIO:       Throughput mb/sec: 94.34
2022-12-27 09:54:23,827 INFO fs.TestDFSIO:  Average IO rate mb/sec: 101.26
2022-12-27 09:54:23,827 INFO fs.TestDFSIO:   IO rate std deviation: 30.07
2022-12-27 09:54:23,827 INFO fs.TestDFSIO:      Test exec time sec: 34.39

3.清理测试数据
hadoop jar hadoop-mapreduce-client-jobclient-3.1.4-tests.jar TestDFSIO -clean

posted @ 2023-02-17 14:52 百衲本阅读(47) 评论(0) 编辑收藏举报

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Mr. Pan

I can because i think i can.

centos7二进制安装Hadoop3

一、Hadoop 简介

1.1 Hadoop3核心组件

1.2 Hadoop集群简介

1.2.1 HDFS

1.2.2 YARN

二、环境信息及准备

2.1 机器及机器角色规划

2.2 节点添加hosts解析

2.3 关闭防火墙

2.4 hdp01到三台机器免密

2.5 时间同步

2.6 调大用户文件描述符

2.7 安装Java环境

三、安装Hadoop

3.1 解压安装包

3.2 修改Hadoop环境配置变量

3.3 指定集群默认配置

3.4 修改SNN配置

3.5 MapReduce配置

3.6 YARN配置

3.7 配置从角色机器地址

8.同步集群配置

四、启动Hadoop

4.1 初始化名称节点

4.2 启动服务

五、验证

5.1 访问相关web UI

5.2 测试创建、上传功能

5.3 测试MapReduce执行

5.4 集群基准测试

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