2020系统综合实践 第4次实践作业

（1）使用Docker-compose实现Tomcat+Nginx负载均衡

参考资料

• 拉取镜像

• 在目录下创建如下文件

• docker-compose.yml

version: "3"
services:
    nginx:
        image: nginx:latest
        ports:
            - "80:80"
        volumes:
            - ./nginx/default.conf:/etc/nginx/conf.d/default.conf 
        depends_on:
            - tomcat1
            - tomcat2
            - tomcat3

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

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

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

• default.conf

upstream tomcats {
    server tomcat1:8080; 
    server tomcat2:8080; 
    server tomcat3:8080; 
}

server {
    listen 80;
    server_name localhost;

    location / {
        proxy_pass http://tomcats; # 请求转向tomcats
    }
}

• 运行docker-compose

sudo docker-compose up -d

• 用浏览器查看

实现nginx的2种负载均衡策略

• 1.负载均衡策略：轮询策略

• test1.py

import requests

for i in range(0,10):
    reponse=requests.get("http://localhost")
    print(reponse.text)

• 2.负载均衡策略：权重策略
• 修改default.conf

upstream tomcats {
    server tomcat1:8080 weight=1; 
    server tomcat2:8080 weight=2; 
    server tomcat3:8080 weight=3; 
}

server {
    listen 80;
    server_name localhost;

    location / {
        proxy_pass http://tomcats; # 请求转向tomcats
    }
}

• 重启容器

• test2.py

import requests
import string
sum1=0
sum2=0
sum3=0
str1='welcome,this is tomcat1
'
str2='welcome,this is tomcat2
'
str3='welcome,this is tomcat3
'
url="http://localhost"
for i in range(0,20):
    response=requests.get(url)
    if response.content == str1:
        sum1=sum1+1
    if response.text == str2:
        sum2=sum2+1
    if response.text == str3:
        sum3=sum3+1
print('welcome,this is tomcat1 sum={}'.format(sum1))
print('welcome,this is tomcat2 sum={}'.format(sum2))
print('welcome,this is tomcat3 sum={}'.format(sum3))

在20次请求返回的结果中，与我们在nginx配置文件中的权重的比值1：2：3基本一致

（2) 使用Docker-compose部署javaweb运行环境

参考资料

• docker-compose.yml

version: "3"   
services:    
  tomcat00:     
    image: tomcat    
    hostname: hostname       
    container_name: tomcat00   
    ports:      
     - "5050:8080"          #后面访问网页的时候要选择对应的端口号5050
    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:     
    image: tomcat    
    hostname: hostname       
    container_name: tomcat01   
    ports:      
     - "5055:8080"
    volumes:  
     - "./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" 

    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
      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

• default.conf

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

server {
    listen 8080;
    server_name localhost;

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

• 修改连接数据库的IP

• 启动容器

sudo docker-compose up -d

• 浏览器访问前端页面

http://127.0.0.1:8080/ssmgrogshop_war
账号：sa        密码：123

• 终端查看数据库

（3）使用Docker搭建大数据集群环境

参考资料

• 实验环境

ubuntu 18.04 LST
openjdk 1.8
hadoop 3.1.3

• 换源
• Dockerfile

FROM ubuntu:18.04
COPY ./sources.list /etc/apt/sources.list

• source.list

# 默认注释了源码镜像以提高 apt update 速度，如有需要可自行取消注释
deb http://mirrors.tuna.tsinghua.edu.cn/ubuntu/ focal main restricted universe multiverse
# deb-src http://mirrors.tuna.tsinghua.edu.cn/ubuntu/ focal main restricted universe multiverse
deb http://mirrors.tuna.tsinghua.edu.cn/ubuntu/ focal-updates main restricted universe multiverse
# deb-src http://mirrors.tuna.tsinghua.edu.cn/ubuntu/ focal-updates main restricted universe multiverse
deb http://mirrors.tuna.tsinghua.edu.cn/ubuntu/ focal-backports main restricted universe multiverse
# deb-src http://mirrors.tuna.tsinghua.edu.cn/ubuntu/ focal-backports main restricted universe multiverse
deb http://mirrors.tuna.tsinghua.edu.cn/ubuntu/ focal-security main restricted universe multiverse
# deb-src http://mirrors.tuna.tsinghua.edu.cn/ubuntu/ focal-security main restricted universe multiverse
# 预发布软件源，不建议启用
# deb http://mirrors.tuna.tsinghua.edu.cn/ubuntu/ focal-proposed main restricted universe multiverse
# deb-src http://mirrors.tuna.tsinghua.edu.cn/ubuntu/ focal-proposed main restricted universe multiverse

• 创建镜像运行容器

sudo docker build -t ubuntu .
sudo docker run -it --name ubuntu ubuntu

• 更新系统软件源并安装vim和ssh

apt-get update
apt-get install vim
apt-get install ssh

• 运行脚本开启sshd服务器并设置自动启动sshd服务

/etc/init.d/ssh start
vim ~/.bashrc

/etc/init.d/ssh start

• 配置sshd

ssh-keygen -t rsa 
cat id_rsa.pub >> authorized_keys

安装JDK

apt-get install openjdk-8-jdk

• 配置环境变量,打开~/.bashrc文件，在最后输入如下内容,并生效

export JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64/
export PATH=$PATH:$JAVA_HOME/bin

• 保存镜像文件

安装Hadoop

• 运行容器

sudo docker run -it -v /home/lin/build:/root/build --name ubuntu-jdkinstalled ubuntu/jdk

• 解压下载好的Hadoop

cd /root/build
tar -zxvf hadoop-3.1.3.tar.gz -C /usr/local

• 查看是否安装

cd /usr/hadoop-3.1.3
./bin/hadoop version

配置Hadoop集群

• hadoop-env.sh

vim hadoop-env.sh
export JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64/

• core-site.xml

<configuration>
  <property>
    <name>hadoop.tmp.dir</name>
    <value>file:/usr/local/hadoop-3.1.3/tmp</value>
    <description>Abase for other temporary directories.</description>
  </property>
  <property>
    <name>fs.defaultFS</name>
    <value>hdfs://master:9000</value>
  </property>
</configuration>

• hdfs-site.xml

<configuration>
    <property>
        <name>dfs.namenode.name.dir</name>
        <value>file:/usr/local/hadoop-3.1.3/namenode_dir</value>
    </property>
    <property>
        <name>dfs.datanode.data.dir</name>
        <value>file:/usr/local/hadoop-3.1.3/datanode_dir</value>
    </property>
    <property>
        <name>dfs.replication</name>
        <value>3</value>
    </property>
</configuration>

• mapred-site.xml

<configuration>
    <property>
        <name>mapreduce.framework.name</name>
        <value>yarn</value>
    </property>
    <property>
        <name>yarn.app.mapreduce.am.env</name>
        <value>HADOOP_MAPRED_HOME=/usr/local/hadoop-3.1.3</value>
    </property>
    <property>
        <name>mapreduce.map.env</name>
        <value>HADOOP_MAPRED_HOME=/usr/local/hadoop-3.1.3</value>
    </property>
    <property>
        <name>mapreduce.reduce.env</name>
        <value>HADOOP_MAPRED_HOME=/usr/local/hadoop-3.1.3</value>
    </property>
</configuration>

• yarn-site.xml

<configuration>
    <property>
        <name>yarn.nodemanager.aux-services</name>
        <value>mapreduce_shuffle</value>
    </property>
    <property>
        <name>yarn.resourcemanager.hostname</name>
        <value>master</value>
    </property>
    <property>
        <name>yarn.nodename.vmem-pmem-ratio</name>
        <value>2.7</value>
    </property>
</configuration>

• 修改启动和结束脚本
• 进入目录

cd /usr/local/hadoop-3.1.3/sbin

• 在start-dfs.sh和 stop-dfs.sh文件中添加

HDFS_DATANODE_USER=root
HADOOP_SECURE_DN_USER=hdfs
HDFS_NAMENODE_USER=root
HDFS_SECONDARYNAMENODE_USER=root

• 在start-yarn.sh和 stop-yarn.sh文件中添加

YARN_RESOURCEMANAGER_USER=root
HADOOP_SECURE_DN_USER=yarn
YARN_NODEMANAGER_USER=root

• 保存镜像

运行Hadoop

• 打开三个终端,分别表示Hadoop集群中的master,slave01和slave02

# 第一个终端
docker run -it -h master --name master ubuntu/hadoopinstalled
# 第二个终端
docker run -it -h slave01 --name slave01 ubuntu/hadoopinstalled
# 第三个终端
docker run -it -h slave02 --name slave02 ubuntu/hadoopinstalled

• 配置master,slave01和slave02的地址信息

vim /etc/hosts

172.17.0.2      master
172.17.0.3      slave01
172.17.0.4      slave02

• 测试ssh

ssh slave01
ssh slave02

• 修改workers文件

vim /usr/local/hadoop-3.1.3/etc/hadoop/workers

localhost修改为：
slave01
slave02

• 启动集群

cd /usr/local/hadoop-3.1.3
bin/hdfs namenode -format
sbin/start-dfs.sh 
sbin/start-yarn.sh

• 运行实例

./bin/hdfs dfs -mkdir -p /user/root/input
./bin/hdfs -put ./etc/hadoop/*.xml /user/root/input 
./bin/hdfs dfs -ls /user/root/input
./bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-3.2.1.jar grep input output 'dfs[a-z.]+' 
./bin/hdfs dfs -cat output/*

（4）主要问题和解决方法

（1）在权重策略测试负载均衡实践中，编写python代码时，没注意到爬到的字符串中带有" "回车，纠结了好久

（2）在开启hadoop集群时没能全部开启，可能是master的workers没有编写的原因（不太确定），删除容器重新操作一下就可以了

（3）虚拟机的内存最好开大一点，不然开hadoop集群的时候可能卡死，实例中文件太多也可能卡死

（4）hadoop集群使用完后要记得关掉

（5）花费时间

这次实验明显比之前的实验复杂，我大约用了两个白天