第4次实践作业

(1)使用Docker-compose实现Tomcat+Nginx负载均衡

反向代理(Reverse Proxy)方式是指以代理服务器来接受Internet上的连接请求,然后将请求转发给内部网络上的服务器,并将从服务器上得到的结果返回给Internet上请求连接的客户端,此时代理服务器对外就表现为一个服务器。
default.conf:

upstream tomcats {
    server tomcat1:8080; # 端口号
    server tomcat2:8080;  
    server tomcat3:8080; 
}

server {
    listen 2526;
    server_name localhost;

    location / {
        proxy_pass http://tomcats; # 轮询访问tomcats
    }
}

docker-compose.yml:

version: "3"
services:
    nginx:
        image: nginx
        container_name: mymynginx
        ports:
            - 80:2526
        volumes:
            - ./default.conf:/etc/nginx/conf.d/default.conf # 挂载配置文件
        depends_on:
            - tomcat1
            - tomcat2
            - tomcat3

    tomcat1:
        image: tomcat
        container_name: tomcat1
        volumes:
           - ./tomcat1:/usr/local/tomcat/webapps/ROOT

    tomcat2:
        image: tomcat
        container_name: tomcat2
        volumes:
           - ./tomcat2:/usr/local/tomcat/webapps/ROOT

    tomcat3:
        image: tomcat
        container_name: tomcat3
        volumes:
           - ./tomcat3:/usr/local/tomcat/webapps/ROOT

查看树形结构:

执行docker-compose文件并查看容器

  • 负载均衡策略1:轮询策略:
import requests

url='http://localhost'
for i in range(0,10):
    response=requests.get(url)
    print(response.text)

  • 负载均衡策略2:权重策略:
upstream tomcats {
    server tomcat1:8080 weight=1; 
    server tomcat2:8080 weight=2;  
    server tomcat3:8080 weight=3; 
}

server {
    listen 2526;
    server_name localhost;

    location / {
        proxy_pass http://tomcats; # 轮询访问tomcats
    }
}

编写python代码:

import requests

url='http://localhost'
count={}
for i in range(0,1000):
    response=requests.get(url)
    if response.text in count:
        count[response.text]+=1;
    else:
        count[response.text]=1
print(count)

(2)使用Docker-compose部署javaweb运行环境

docker-compose.yml:

version: "3"   #版本
services:     #服务节点
  tomcat00:     #tomcat 服务
    image: tomcat    #镜像
    hostname: hostname       #容器的主机名
    container_name: tomcat00   #容器名
    ports:      #端口
     - "5050:8080"
    volumes:  #数据卷
     - "./webapps:/usr/local/tomcat/webapps"
     - ./wait-for-it.sh:/wait-for-it.sh
    networks:   #网络设置静态IP
      webnet:
        ipv4_address: 15.22.0.15
  tomcat01:     #tomcat 服务
    image: tomcat    #镜像
    hostname: hostname       #容器的主机名
    container_name: tomcat01   #容器名
    ports:      #端口
     - "5055:8080"
    volumes:  #数据卷
     - "./webapps:/usr/local/tomcat/webapps"
     - ./wait-for-it.sh:/wait-for-it.sh
    networks:   #网络设置静态IP
      webnet:
        ipv4_address: 15.22.0.16
  mymysql:  #mymysql服务
    build: .   #通过MySQL的Dockerfile文件构建MySQL
    image: mymysql:test
    container_name: mymysql
    ports:
      - "3309:3306" 
#红色的外部访问端口不修改的情况下,要把Linux的MySQL服务停掉
#service mysql stop
#反之,将3306换成其它的
    command: [
            '--character-set-server=utf8mb4',
            '--collation-server=utf8mb4_unicode_ci'
    ]
    environment:
      MYSQL_ROOT_PASSWORD: "123456"
    networks:
      webnet:
        ipv4_address: 15.22.0.6
  nginx:
      image: nginx
      container_name: "nginx-tomcat"
      ports:
          - 8080:8080
      volumes:
          - ./default.conf:/etc/nginx/conf.d/default.conf # 挂载配置文件
      tty: true
      stdin_open: true
      depends_on:
          - tomcat00
          - tomcat01
      networks:
       webnet:
        ipv4_address: 15.22.0.7
networks:   #网络设置
 webnet:
   driver: bridge  #网桥模式
   ipam:
     config:
      - 
       subnet: 15.22.0.0/24   #子网

docker-entrypoint.sh:

#!/bin/bash
mysql -uroot -p123456 << EOF    #  << EOF 必须要有
source /usr/local/grogshop.sql;
Dockerfile
#  这个是构建MySQL的dockerfile
FROM registry.saas.hand-china.com/tools/mysql:5.7.17
# mysql的工作位置
ENV WORK_PATH /usr/local/
# 定义会被容器自动执行的目录
ENV AUTO_RUN_DIR /docker-entrypoint-initdb.d
#复制gropshop.sql到/usr/local 
COPY grogshop.sql  /usr/local/
#把要执行的shell文件放到/docker-entrypoint-initdb.d/目录下,容器会自动执行这个shell
COPY docker-entrypoint.sh  $AUTO_RUN_DIR/
#给执行文件增加可执行权限
RUN chmod a+x $AUTO_RUN_DIR/docker-entrypoint.sh
# 设置容器启动时执行的命令
#CMD ["sh", "/docker-entrypoint-initdb.d/import.sh"]

default.conf:

upstream tomcat123 {
    server tomcat00:8080;
    server tomcat01:8080;
}

server {
    listen 8080;
    server_name localhost;

    location / {
        proxy_pass http://tomcat123;
    }
}

进入项目对应目录修改连接数据库的IP
cd /home/compose/webapps/ssmgrogshop_war/WEB-INF/classes
启动容器:
docker-compose up -d

打开浏览器访问:localhost:8080/ssmgrogshop_war

(3)使用Docker搭建大数据集群环境

1.pull ubuntu镜像

拉取镜像并在个人文件下创建一个目录,用于向Docker内部的Ubuntu系统传输文件,创建并运行容器

docker pull ubuntu
cd ~
mkdir build
sudo docker run -it -v /home/zhou/build:/root/build --name ubuntu ubuntu

2.Ubuntu容器的初始化

(1)进入容器中先换源,这里选用阿里源
cat<<EOF>/etc/apt/sources.list      
deb http://mirrors.aliyun.com/ubuntu/ bionic main restricted universe multiverse
deb-src http://mirrors.aliyun.com/ubuntu/ bionic main restricted universe multiverse
deb http://mirrors.aliyun.com/ubuntu/ bionic-security main restricted universe multiverse
deb-src http://mirrors.aliyun.com/ubuntu/ bionic-security main restricted universe multiverse
deb http://mirrors.aliyun.com/ubuntu/ bionic-updates main restricted universe multiverse
deb-src http://mirrors.aliyun.com/ubuntu/ bionic-updates main restricted universe multiverse
deb http://mirrors.aliyun.com/ubuntu/ bionic-backports main restricted universe multiverse
deb-src http://mirrors.aliyun.com/ubuntu/ bionic-backports main restricted universe multiverse
deb http://mirrors.aliyun.com/ubuntu/ bionic-proposed main restricted universe multiverse
deb-src http://mirrors.aliyun.com/ubuntu/ bionic-proposed main restricted universe multiverse
EOF
(2)ubuntu环境的初始化
apt-get update
# 安装vim软件
apt-get install vim     
# 安装sshd,因为在开启分布式Hadoop时,需要用到ssh连接slave: 
apt-get install ssh 
# 运行脚本即可开启sshd服务器    
/etc/init.d/ssh start     
vim ~/.bashrc             
# 在该文件中最后一行添加如下内容,实现进入Ubuntu系统时,都能自动启动sshd服务
/etc/init.d/ssh start 


配置ssh:

ssh-keygen -t rsa # 一直按回车即可
cd ~/.ssh
cat id_rsa.pub >> authorized_keys 
(3)为容器安装SDK

①安装JDK,这里使用JDK8版本

apt-get install openjdk-8-jdk
vim ~/.bashrc       # 在文件末尾添加以下两行,配置Java环境变量:
export JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64/
export PATH=$PATH:$JAVA_HOME/bin
source ~/.bashrc # 使.bashrc生效

②docker commit从容器去创建一个镜像

#另开一个终端
sudo docker commit 容器id ubuntu-jdk8      #讲其保存说明是jkd8版本的ubuntu
sudo docker run -it -v /home/zhou/build:/root/build --name ubuntu-jdk8 ubuntu-jdk8
#开启保存的那份镜像ubuntu-jdk8
(4)安装Hadoop
cd /root/build
tar -zxvf hadoop-3.1.3.tar.gz -C /usr/local #将hadoop压缩包放入本地build文件夹中,这里使用大数据实验中的3.1.3版本
cd /usr/local/hadoop-3.1.3
./bin/hadoop version # 验证安装
(5)配置Hadoop集群
hadoop-env.sh
cd /usr/local/hadoop-3.1.3/etc/hadoop #进入配置文件存放目录
vim hadoop-env.sh
export JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64/ # 在任意位置添加

① core-site.xml

vim core-site.xml
<configuration>
      <property>
          <name>hadoop.tmp.dir</name>
          <value>file:/usr/local/hadoop-3.1.3/tmp</value>
          <description>A base for other temporary directories.</description>
      </property>
      <property>
          <name>fs.defaultFS</name>
          <value>hdfs://master:9000</value>
      </property>
</configuration>

② hdfs-site.xml

vim hdfs-site.xml
<configuration>
        <property>
        <name>dfs.replication</name>
        <value>1</value>
    </property>
    <property>
        <name>dfs.namenode.name.dir</name>
        <value>file:/usr/local/hadoop-3.1.3/tmp/dfs/name</value>
    </property>
    <property>
        <name>dfs.namenode.data.dir</name>
        <value>file:/usr/local/hadoop-3.1.3/tmp/dfs/data</value>
    </property>
</configuration>

③ mapred-site.xml

vim mapred-site.xml
<configuration>
	<property>
		<!--使用yarn运行MapReduce程序-->
		<name>mapreduce.framework.name</name>
		<value>yarn</value>
	</property>
	<property>
		<!--jobhistory地址host:port-->
		<name>mapreduce.jobhistory.address</name>
		<value>master:10020</value>
	</property>
	<property>
		<!--jobhistory的web地址host:port-->
		<name>mapreduce.jobhistory.webapp.address</name>
		<value>master:19888</value>
	</property>
	<property>
		<!--指定MR应用程序的类路径-->
		<name>mapreduce.application.classpath</name>
		<value>/usr/local/hadoop-3.1.3/share/hadoop/mapreduce/lib/*,/usr/local/hadoop-3.1.3/share/hadoop/mapreduce/*</value>
	</property>
</configuration>

④ yarn-site.xml

vim yarn-site.xml
<configuration>
	<property>
		<name>yarn.resourcemanager.hostname</name>
		<value>master</value>
	</property>
	<property>
		<name>yarn.nodemanager.aux-services</name>
		<value>mapreduce_shuffle</value>
	</property>
	<property>
		<name>yarn.nodemanager.vmem-pmem-ratio</name>
		<value>2.5</value>
	</property>
</configuration>

⑤ 修改脚本
对于start-dfs.sh和stop-dfs.sh文件,添加下列参数:

cd /usr/local/hadoop-3.1.3/sbin
HDFS_DATANODE_USER=root
HADOOP_SECURE_DN_USER=hdfs
HDFS_NAMENODE_USER=root
HDFS_SECONDARYNAMENODE_USER=root

对于start-yarn.sh和stop-yarn.sh,添加下列参数:

YARN_RESOURCEMANAGER_USER=root
HADOOP_SECURE_DN_USER=yarn
YARN_NODEMANAGER_USER=root
(6)运行Hadoop集群

保存镜像
sudo docker commit 容器id ubuntu/hadoopinstalled
从三个终端分别开启三个容器运行ubuntu/hadoopinstalled镜像,分别表示Hadoop集群中的master,slave01和slave02

# 第一个终端

sudo docker run -it -h master --name master ubuntu/hadoopinstalled
# 第二个终端
sudo docker run -it -h slave01 --name slave01 ubuntu/hadoopinstalled
# 第三个终端
sudo docker run -it -h slave02 --name slave02 ubuntu/hadoopinstalled

三个终端分别打开/etc/hosts,根据各自ip修改为如下形式

cat /etc/hosts
172.17.0.3	master
172.17.0.4      slave01
172.17.0.5      slave02

(7)测试ssh

在master结点测试ssh,连接到slave结点

ssh slave01
ssh slave02
exit 退出

修改master上workers文件;将localhost修改为如下所示

vim /usr/local/hadoop-3.1.3/etc/hadoop/workers
slave01
slave02
(8)测试Hadoop集群

① 在master终端上执行

cd /usr/local/hadoop-3.1.3
bin/hdfs namenode -format      #首次启动Hadoop需要格式化
sbin/start-all.sh              #启动所有服务

② 使用jps查看三个终端,如图所示

③ 建立HDFS文件夹

bin/hdfs dfs -mkdir /user 
bin/hdfs dfs -mkdir /user/root      #注意input文件夹是在root目录下
bin/hdfs dfs -mkdir input
在master终端上vim一个测试样例,并将其上传到input文件夹;注意test文件的路径
bin/hdfs dfs -put ~/test.txt input
(9)运行hadoop 自带的测试实例

① 准备测试样例的输入文件

bin/hdfs dfs -mkdir -p /user/hadoop/input
bin/hdfs dfs -put ./etc/hadoop/*.xml /user/hadoop/input
bin/hdfs dfs -ls /user/hadoop/input //可以看到九个文件

② 测试样例并输出结果

bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.3.jar grep /user/hadoop/input output 'dfs[a-z.]+'    //运行的时候很慢,需要等待
bin/hdfs dfs -cat output/*       //输出结果

出现的问题

1️⃣执行docker-compose文件出错
忘记出错的原因了,也没有截图( ′◔ ‸◔`)
2️⃣运行test.py文件时出错
./test.py改成python3 test.py
3️⃣电脑连的手机热点,因为被限速了,下载的很慢,心好累
该死的校园卡
具体的使用时间没有计算,但从Chrome的历史记录可以粗略地估计一下花了十几个小时,这还是在参考了大佬的博客的基础上,在下载安装上浪费了一些时间

posted @ 2020-05-18 16:22  焰心无泪  阅读(202)  评论(0编辑  收藏  举报