# 拉取 Elasticsearch 镜像
docker pull elasticsearch:8.6.0

# 拉取 Logstash 镜像
docker pull logstash:8.6.0
   
# 拉取 Kibana 镜像
docker pull kibana:8.6.0

# 部署 Elasticsearch
docker run -d --name elasticsearch -e "discovery.type=single-node" -e "ELASTIC_PASSWORD=qweasdzxc" -e "xpack.security.enabled=false" -e "ES_JAVA_OPTS=-Xms512m -Xmx512m" -p 9200:9200 -p 9300:9300 --network=cicd_network elasticsearch:8.6.0

# 部署 Kibana
docker run -d --name kibana -e "ELASTICSEARCH_HOSTS=http://192.168.1.1:9200" -e "ELASTICSEARCH_PASSWORD=qweasdzxc" -e "I18N_LOCALE=zh-CN" -p 5601:5601 --network=cicd_network kibana:8.6.0

# 部署 Logstash
docker run -d --name logstash -v /usr/logstash.conf:/usr/share/logstash/pipeline/logstash.conf -v /logs/app.log:/logs/app.log -p 5044:5044 --network=cicd_network logstash:8.6.0

# 访问Kibana http://localhost:5601
Analytics-》Discover-》创建数据视图-》开始搜索日志

# logstash.conf
input {
  file {
    path => "/logs/app.log"
    start_position => "beginning"
    stat_interval => "3"
    type => "app_log"
  }
}
output {
   if [type] == "app_log" {
     elasticsearch {
       hosts => ["http://192.168.1.1:9200"]
       index => "app_log-%{+YYYY.MM.dd}"
    }
  }
}

<?xml version="1.0" encoding="UTF-8"?>
<configuration>
    <!--日志文件所在目录-->
    <property name="LOG_HOME" value="/logs" />

    <!--采用打印到控制台，记录日志的方式-->
    <appender name="console" class="ch.qos.logback.core.ConsoleAppender">
        <encoder>
            <!--日志输出格式-->
            <pattern>%d{yyyy-MM-dd HH:mm:ss.SSS} [%thread] %-5level %logger{36} - %msg%n</pattern>
        </encoder>
    </appender>

    <!-- 采用保存到日志文件 记录日志的方式，
    %d{yyyy-MM-dd HH:mm:ss.SSS}：输出日志发生的时间，精确到毫秒。
    [%thread]：输出日志所在的线程名。
    %-5level：输出日志级别，使用占位符%5level可以保持日志级别的对齐。
    %logger{36}：输出日志所在的类名（只输出类名的后36个字符）。
    %msg：输出日志消息。
    %n：新行。
    -->
    <!--滚动记录文件-->
    <appender name="rollingFile" class="ch.qos.logback.core.rolling.RollingFileAppender">
        <!--日志文件地址-->
        <file>${LOG_HOME}/app.log</file>
        <rollingPolicy class="ch.qos.logback.core.rolling.TimeBasedRollingPolicy">
            <!--粒度：天。等价于一天一个文件-->
            <fileNamePattern>${LOG_HOME}/appLog-%d{yyyy-MM-dd}.log</fileNamePattern>
        </rollingPolicy>
        <encoder>
            <pattern>%d{yyyy-MM-dd HH:mm:ss.SSS} [%thread] %-5level %logger{36} - %msg%n</pattern>
        </encoder>
    </appender>

    <root level="INFO" additivity="false">
        <appender-ref ref="rollingFile" />
        <appender-ref ref="console" />
    </root>
</configuration>

# SQL脚本
CREATE DATABASE kuboard DEFAULT CHARACTER SET = 'utf8mb4' DEFAULT COLLATE = 'utf8mb4_unicode_ci';
create user 'kuboard'@'%' identified by 'Kuboard123';
grant all privileges on kuboard.* to 'kuboard'@'%';
FLUSH PRIVILEGES;

# 拉取 Kibana 镜像
docker run -d --restart=unless-stopped --name=kuboard -p 8090:80/tcp -e TZ="Asia/Shanghai" -e DB_DRIVER=com.mysql.cj.jdbc.Driver -e DB_URL="jdbc:mysql://192.168.159.1:3306/kuboard?serverTimezone=Asia/Shanghai" -e DB_USERNAME=kuboard -e DB_PASSWORD=Kuboard123 --network=cicd_network swr.cn-east-2.myhuaweicloud.com/kuboard/kuboard:v3

# Centos7 系统初始化
docker pull centos:7.9.2009
docker network create deepseek_network
docker run --network deepseek_network -d -it -p 8080:8080 -p 80:80 -p 443:443 -p 11434:11434 --privileged=true --name deepseek-server centos:7.9.2009 /usr/sbin/init

# DeepSeek 服务端部署
curl -fsSL https://ollama.com/install.sh | sh
vi /etc/systemd/system/ollama.service
# =>[Service]部分Environment之后增加"OLLAMA_HOST=0.0.0.0"
systemctl daemon-reload
systemctl restart ollama

# 运行 Deepseek-R1【推理模型 1.1GB】
ollama run deepseek-r1:1.5b

# 运行 Deepseek-V3【通用大模型 404GB】
ollama run deepseek-v3

# DeepSeek WebUI可视化部署【192.168.1.1需替换为本机IP】
docker run --network deepseek_network -d -p 3000:8080 -e OLLAMA_BASE_URL=http://192.168.1.1:11434 -v open-webui:/app/backend/data --name deepseek-webui --restart always ghcr.io/open-webui/open-webui:main

# 李想已封装好的，开箱即用的DeepSeek-Server【已内置R1模型】
docker network create deepseek_network
docker run --network deepseek_network -d -it -p 8080:8080 -p 80:80 -p 443:443 -p 11434:11434 --privileged=true --name deepseek-server1 caoofduty/deepseek-server:dev /usr/sbin/init

# DeepSeek WebUI可视化部署【192.168.1.1需替换为本机IP】
docker run --network deepseek_network -d -p 3000:8080 -e OLLAMA_BASE_URL=http://192.168.159.1:11434 -v open-webui:/app/backend/data --name deepseek-webui1 --restart always ghcr.io/open-webui/open-webui:main

# 尽情使用吧~ http://127.0.0.1:3000

# 使用【已有服务器 或 新部署的Centos】
docker pull centos:8
docker run --network cicd_network -d -it -p 8888:8888 --name=harbor --privileged=true centos:8 /usr/sbin/init

# 进入容器-指定系统环境地址
cd /etc/yum.repos.d/
sed -i 's/mirrorlist/#mirrorlist/g' /etc/yum.repos.d/CentOS-*
sed -i 's|#baseurl=http://mirror.centos.org|baseurl=http://vault.centos.org|g' /etc/yum.repos.d/CentOS-*

# 其他系统软件安装 
cd /opt
dnf install wget
wget -O /etc/yum.repos.d/CentOS-Base.repo https://mirrors.aliyun.com/repo/Centos-vault-8.5.2111.repo
yum clean all
yum makecache

# 安装Docker环境
cd /opt
curl -fsSL https://get.docker.com -o get-docker.sh
sh get-docker.sh

yum install systemd-sysv
systemctl start docker
systemctl enable docker

# Harbor源码
cd /opt/harbor
curl -SL https://github.com/goharbor/harbor/releases/download/v2.7.1/harbor-offline-installer-v2.7.1.tgz
tar xf harbor-offline-installer-v2.7.1.tgz

# 克隆编辑配置
cp harbor.yml.tmpl harbor.yml
vi harbor.yml

hostname: 192.168.1.1
port: 8888
注释:https 相关代码

# Harbor安装
cd /opt/harbor
./install.sh

# Harbor后台地址: http://192.168.1.1:8888
管理员账户= admin
管理员密码= Harbor12345

# 新建项目 和 用户
用户账户= test
用户密码= Qweasdzxc123

# Docker调整引擎配置
{
    "registry-mirrors": [
        "https://registry.docker-cn.com"
    ],
    "insecure-registries": [
        "192.168.1.1:8888"
    ]
}
# 刷新配置&&重启Docker

# Docker使用私有镜像仓库
# 登入
docker login -u test -p Qweasdzxc123 http://192.168.1.1:8888

# 登出
docker logout

# 拉取镜像
docker pull grafana/grafana

# Grafana服务运行
docker run --network cicd_network -d -p 5000:3000 --name=grafana -v grafana-storage:/var/lib/grafana grafana/grafana
  
# 进入后台 初始密码【admin/admin】
http://192.168.1.1:5000/login

# 拉取镜像
docker pull prom/prometheus

# 配置文件
mkdir /opt/prometheus
vi /opt/prometheus/prometheus.yml #内容见附件 或下载 https://lixiangpro.oss-cn-hangzhou.aliyuncs.com/resource/prometheus.yml

# 普罗米修斯运行
docker run --network cicd_network -d -p 9090:9090 --name=prometheus -v /opt/prometheus/prometheus.yml:/etc/prometheus/prometheus.yml prom/prometheus

# Node_Exporter 运行
docker run --network cicd_network -d -p 9100:9100 --name=node-exporter -v "/proc:/host/proc:ro" -v "/sys:/host/sys:ro" -v "/:/rootfs:ro" --net="host" prom/node-exporter

# 重启普罗米修斯
docker restart prometheus

# 进入Grafana后台 http://192.168.1.1:5000/login

# 配置数据源
=》首页-连接-数据源-Add data source-prometheus
Prometheus server URL = http://192.168.1.1:9090

# 导入看板视图模版【查看更多=> https://grafana.com/grafana/dashboards】
DEMO模版 = https://lixiangpro.oss-cn-hangzhou.aliyuncs.com/resource/grafana_node_exporter.json
=》首页-仪表盘-新建-导入

# 设置默认首页看板
=》首页-管理-概况-默认首选项-主页仪表板

# 部署MySQL
docker pull mysql:8.0.32
docker run -p 3306:3306 --name=mysql --network cicd_network --restart=always --privileged=true -v /opt/mysql/conf:/etc/mysql/conf.d -v /opt/mysql/logs:/var/log -v /opt/mysql/data:/var/lib/mysql -v /opt/mysql/mysql-files:/var/lib/mysql-files -e MYSQL_ROOT_PASSWORD=123456 -d mysql:8.0.32

# 部署MySQL Exporter
vi /opt/mysql/my.cnf
[client]
host=192.168.1.1
user=root
password=123456

docker pull prom/mysqld-exporter
docker run -d --name mysql_exporter --restart always -p 9104:9104  -v /opt/mysql/conf:/.my.cnf --network=cicd_network prom/mysqld-exporter

# 修改普罗米修斯配置
vi /opt/prometheus/prometheus.yml
  - job_name: mysql-monitor
    static_configs:
      - targets: [ '192.168.1.1:9104' ]
      
# 重启普罗米修斯
docker restart prometheus

# 进入Grafana后台 http://192.168.1.1:5000/login
=》首页-仪表板-导入-模版ID=7362-加载-prometheus

# 直连监控
=》首页-连接-添加新链接-Redis-install-Add new data source-Dashboards-Redis Import

# 本地部署Redis
docker pull redis:latest
docker run --name redis -d -p 6379:6379 -e REDIS_PASSWORD=123456 --network cicd_network redis:latest

# 本地部署Redis管理工具
docker pull redis/redisinsight:latest
docker run -d --name redisinsight -p 5540:5540 --network cicd_network redis/redisinsight:latest

# 修改普罗米修斯配置
vi /opt/prometheus/prometheus.yml
  - job_name: redis
    static_configs:
      - targets: [ '192.168.1.1:9121' ]
        labels:
          instance: redis

# 本地部署Redis管理工具
docker pull oliver006/redis_exporter:v1.45.0
docker run -d --name redis-exporter -p 9121:9121 -e REDIS_ADDR=redis://192.168.1.1:6379  -e REDIS_USER=root -e REDIS_PASSWORD=123456 oliver006/redis_exporter:v1.45.0

# 重启普罗米修斯
docker restart prometheus

# 进入Grafana后台 http://192.168.1.1:5000/login
=》首页-仪表板-导入-模版ID=763-加载-prometheus

# Grafana配置飞书支持告警
=》警报->警报规则->New alert rule
 填写告警名称->填写查询SQL->Expressions定义条件（比如计数汇总>10）->定义执行频率->选择告警WebHook
# 使用效果: Grafana自动执行SQL查询业务数据,满足自己定义的条件后,可以直接发送告警数据到群聊等【飞书机器人助手】。

# 自定义的Grafana飞书告警
=》警报->联络点->Create contact point
  填写联络点名称->整合模版选择->Webhook->配置推送地址和请求方式
# 使用效果:可以把Grafana原始数据推送到这里的Url。后端服务,解析数据并持久化,可自定义加工并发送消息到群聊。

global:
  scrape_interval:     15s
  evaluation_interval: 15s
scrape_configs:
  - job_name: prometheus
    static_configs:
      - targets: ['192.168.1.1:9090']
        labels:
          instance: centos7
  - job_name: grafana
    static_configs:
      - targets: ['192.168.1.1:9100']
        labels:
          instance: centos7
  - job_name: hello_world
    static_configs:
      - targets: ['192.168.1.1:8080']
        labels:
          instance: centos7

# 创建公共网络
docker network create cicd_network

# 下载GitLab镜像
docker pull gitlab/gitlab-ce

# 运行GitLab服务
docker run --network cicd_network -d -p 443:443 -p 80:80 -p 22:22 --name gitlab --restart always -v /home/gitlab/config:/home/gitlab/config -v /home/gitlab/logs:/home/gitlab/logs -v /home/gitlab/data:/home/gitlab/data gitlab/gitlab-ce

# 进入容器->查看管理员密码
docker exec -it gitlab /bin/bash
cat /etc/gitlab/initial_root_password

# 管理员账号: root
# 管理员密码: Mi4FZWvbeIghqMALXGZd7IkxmIfHNrmLZpnhab8Daf8=

# 进入容器->修改配置[首次部署需等待容器内安装完成后再修改,避免产生其他问题]
docker exec -it gitlab /bin/bash
vi /etc/gitlab/gitlab.rb

external_url='http://192.168.1.1'
gitlab_rails['gitlab_ssh_host'] = '192.168.1.1'
gitlab_rails['gitlab_shell_ssh_port'] = 22

# GitLab外部网络设置Allow【Admin->Settings->Network->Outbound requests】

# 重启服务
docker restart gitlab

# 登录GitLab
http://192.168.1.1/users/sign_in

# GitLab创建应用【Admin->Applications->add New application】
RedirectURI= http://192.168.1.1:2080/login
ApplicationID= 1e50d47b793a8458a62b033a2d436e2b62be2df2518218fa130b432794635c74
Secret= gloas-bf787c02e057df6c42f772547ae3f8a3c153b6213c025450685219eeeb07448d

# 生成本地 SSH-Key
ssh-keygen -t rsa -b 4096 -C "lixiang@tron.com"
# SSH-Key 添加至GitLab【点击头像->Preferences->SSH Keys->add New key】

# Hello-World Java项目初始化及代码提交
下载地址 = https://lixiangpro.oss-cn-hangzhou.aliyuncs.com/resource/hello-world.rar

# 拉取镜像
docker pull drone/drone:2
docker pull drone/drone-runner-docker

# 运行 Drone-server
docker run --network cicd_network --volume=/var/lib/drone:/data --env=DRONE_AGENTS_ENABLED=true --env=DRONE_GITLAB_SERVER=http://192.168.1.1 --env=DRONE_GITLAB_CLIENT_ID=0b7a89352763fffc7a53ff5ebc5a49472376f8f04075d24a3d43354049915bac --env=DRONE_GITLAB_CLIENT_SECRET=gloas-932f99b89bd76ae921e0b994ac8ec47ca764d660ed86d8e7e913958749ad2b1e --env=DRONE_SERVER_HOST=192.168.1.1:2080 --env=DRONE_SERVER_PROTO=http --env=DRONE_TLS_AUTOCERT=false --env=DRONE_LOGS_DEBUG=true -e DRONE_RPC_SECRET=033a2d436e2b62be2df2518218fa130 --env=DRONE_USER_CREATE=username:root,admin:true -e TZ="Asia/Shanghai" --publish=2080:80 --publish=2443:443 --restart=always --detach=true --name=drone-server drone/drone:2
  
# 运行 Drone-runner
docker run --network cicd_network -d -v /var/run/docker.sock:/var/run/docker.sock -e DRONE_RPC_PROTO=http -e DRONE_RPC_HOST=192.168.1.1:2080 -e DRONE_RPC_SECRET=033a2d436e2b62be2df2518218fa130 -e DRONE_RUNNER_CAPACITY=2 -e DRONE_RUNNER_NAME=drone-runner -e TZ="Asia/Shanghai" -p 3000:3000 --restart always --name drone-runner drone/drone-runner-docker

# Drone后台 http://192.168.1.1:2080
修改配置【项目根目录/Settings/Project Settings】调整为Trust等

# GitLab项目配置WEBHOOK
 进入GitLab后台【项目根目录->Settings->Webhooks】http://192.168.1.1
 检查确认WebHook地址URL = http://192.168.1.1:2080/hook

# 代码master分支提交更改 验收自动化持续集成部署

kind: pipeline
type: docker
name: Hello_World
clone:
disable: true  #禁用默认克隆
trigger:
branch:
- master  #仅在 master分支的代码提交,才触发部署
steps:
- name: 克隆
  image: alpine/git
  pull: if-not-exists
  commands:
    - git version
    - git clone http://192.168.1.1/root/hello-world.git
    - cd hello-world && git checkout master

- name: 打包
  image: caoofduty/maven_jdk_17:v3 # maven:3.6.3-openjdk-17
  pull: if-not-exists
  volumes:
  # 挂载宿主机的目录
    - name: maven-build
      path: /data/build
      commands:
    - cd hello-world
    - mvn -Dmaven.test.skip=true -Dmaven.wagon.http.ssl.insecure=true -Dmaven.wagon.http.ssl.allowall=true package
    - mkdir -p /data/build
    - cp target/*.jar /data/build
    - cp run.sh /data/build
    - cp Dockerfile /data/build

- name: 构建镜像
  image: plugins/docker
  volumes:
    - name: maven-build
      path: /data/build
    - name: docker
      path: /var/run/docker.sock # 挂载宿主机的docker
      settings:
      dockerfile: /data/build/Dockerfile
      commands:
    - cd /data/build
    - docker --version
    - docker build -t hello_world:latest -f /data/build/Dockerfile .
    - docker images

- name: 发布
  image: plugins/docker
  volumes:
    - name: maven-build
      path: /data/build
    - name: docker
      path: /var/run/docker.sock # 挂载宿主机的docker
      settings:
      dockerfile: /data/build/Dockerfile
      commands:
    - cd /data/build
    - docker --version
    - docker images
    - docker run -d --name hello_world -p 8080:8080 -e TZ="Asia/Shanghai" hello_world:latest

- name: 成功了,庆祝~
  image: alpine
  commands:
    - echo hello world

volumes:
- name: maven-build
  host:
  path: /data/build # 从宿主机中挂载的目录
- name: docker
  host:
  path: /var/run/docker.sock
#  - name: maven-cache
#    host:
#      path: /data/cache # 从宿主机中挂载的目录

FROM openjdk:17

ADD hello-world-0.0.1-SNAPSHOT.jar /home/app.jar

EXPOSE 8080

CMD ["java", "-jar", "/home/app.jar"]

# SQL逻辑: 版本号MVCC 同时只有库存>0才扣减库存
UPDATE stock = stock - 1 WHERE stock = oldStock AND stock>0 AND product_id = 123;

# 库存字段值不可以为负数
ALTER TABLE products MODIFY COLUMN stock UNSIGNED NOT NULL DEFAULT 0;

# 方案一: Redis+Lua 保证一组操作的原子性，同时避免死锁。
Object seckillCount = redisTemplate.execute(script, Arrays.asList(SeckillRedisKey.SECKILL_STOCK_COUNT_HASH.getRealKey(round)), "-1"); 
# 方案二: Redis Increment 
Long seckillCount = redisTemplate.opsForHash().increment(SeckillRedisKey.SECKILL_STOCK_COUNT_HASH.getRealKey(round),seckillId, -1);

// 创建锁对象-Redission内置看门狗，自动锁续期。
RLock lock = redissonClient.getLock("seckill:product:stock:lock:" + seckillId);
SeckillProductVo vo = null;
try {
    // 加锁
    lock.lock(10, TimeUnit.SECONDS);
    // 查询最新的库存情况【DB直查】
    vo = seckillProductService.findById(seckillId);
    if (vo.getStockCount() > 0) { // 库存大于 0 才扣库存
        // 扣除库存【乐观锁】
        int row = seckillProductService.decrStockCount(seckillId);
        if (row <= 0) {
           // 库存数不足
           throw new BusinessException(SeckillCodeMsg.SECKILL_STOCK_OVER);
        }
    }
    // 创建订单对象
    OrderInfo orderInfo = this.create(userId, vo);
    // 保存订单对象
    orderInfoMapper.insert(orderInfo);
    
    return orderInfo.getOrderNo();// 秒杀成功，订单落库，恭喜用户～
} catch (BusinessException be) {
    throw be; // 不处理继续往外抛出
} catch (Exception e) {
    // 重新同步 redis 库存，设置本地库存售完标识为 false
    if (vo != null && vo.getStockCount() > 0) {
        this.rollbackStockCount(vo);
    }
    // 继续向外抛出异常
    throw new BusinessException(500,"SYSTEM_ERROR");
} finally {
    // 释放当前线程锁
    if(lock.isHeldByCurrentThread() && lock.isLocked()){
       lock.unlock();
    }
}

# 单机场景下，可以直接使用同步锁，性能较差，不支持分布式微服务场景。
synchronized(this){
   // 库存查询扣减等业务逻辑
};

# 1.前置进行各种业务校验，检查登录，检查活动是否进行，是否在灰度策略，挡掉大部分流量。
# 2.JVM本地缓存一份已经抢光的商品MAP，若存在该商品，直接返回，提高响应速度。【如果进行库存补偿，需要各POD都清空此处MAP】
public static final Map<Long, Boolean> LOCAL_STOCK_OVER_FALG_MAP = new ConcurrentHashMap<>();
# 3.检查用户是否已秒杀过该商品
# 4.Redis库存量检查
Long seckillCount = redisTemplate.opsForHash().increment(SeckillRedisKey.SECKILL_STOCK_COUNT_HASH.getRealKey(round),seckillId, -1);
if (seckillCount < 0) {
   // 标记该秒杀商品已经卖完了
   LOCAL_STOCK_OVER_FALG_MAP.put(seckillId, true);
   return;
}
# 5.代码编写时，注意并发请求是否会对最终结果产生影响，注意加锁。
# 6.锁优化，降低锁的颗粒度，释放锁只释放本线程上的锁。
# 7.扣减库存持久化到DB。
# =====其他=====>
# 8.使用中间件，流量削峰，异步串行处理。
# 9.异步Servelt，提高吞吐量，提高性能。
# 10.提前评估请求量，服务端负载均衡，数据库多实例，缓存层多级缓存。理论上Nginx支持20w级别并发，Redis单机至高10w，MySQL至多几千，同时也受硬件磁盘读写能力影响。Java的SpringBoot内置的Tomcat上千左右。上线前也要考虑，是否进行配置调参优化或改用其他。 
# 11.热点数据提前预热等等，各大电商公司，真实的生产解决方案应该会更高效和全面，希望有一天也有机会可以见识下。

# 复制
cp -r folder1 folder2
# 压缩
tar zcf folder.tar.gz folder
# 解压
tar zxvf filename.tar
# 传输文件到本地桌面
sz filename
# 传输文件到远程机器
scp filename.tar.gz root@192.168.0.1:/opt
# 查找文件夹内运行的进程
ps -ef|grep 文件夹
# 杀掉进程
kill -9 123
# 指定目录下查找文件
find /usr -name 'a' 
# 模糊查找文件
grep -r 'XXX' /
# 查看文件中包含的关键字
grep "keyword" xxx.log
cat xxx.log | grep keyword

# 定时脚本 
yum install crontabs
# 编辑任务 no.1
crontab -e
# 编辑任务 no.2
vim /etc/crontab  
# 定时命令
*/1 * * * * ab -n 3000 -c 500 http://localhost:8080/
# 启动定时服务
/sbin/service crond start
# 关闭定时服务
/sbin/service crond stop
# 重启定时服务
/sbin/service crond restart
# 重新载入定时配置
/sbin/service crond reload
# 查看crontab定时服务状态
service crond status
# 手动启动crontab定时服务
service crond start

# 搜索指定端口
netstat -an | grep 8080
# 关闭防火墙
chkconfig iptables off
# 重启网络
service network restart

# 内存
free
# 磁盘
df
# 磁盘IO
iostat
# 网络IO
ifstat
# 进程
top
# 查看占用CPU最高的进程对应线程,可得到线程ID tid
top -H-p pid
# 线程ID转为16进制
printf ‘%x’ tid
# 查看进程中占用CPU最高的线程，发现是GC线程
jstack pid | grep -C 5 tid
# 查下GC情况
jstat -gc pid 500
# dump出JVM堆内存的数据
jmap -dump:format=b,file=/tmp/**.dump pid
# 分析堆快照
visualvm mat

# 查看topic列表
./kafka-topics.sh --list --zookeeper 192.168.0.1:2181
# 查看消费者列表
./kafka-consumer-groups.sh --bootstrap-server 192.168.0.1:9092 --list
# 查看指定分组的消费情况
./kafka-consumer-groups.sh --bootstrap-server 192.168.0.1:9092,192.168.0.2:9092,192.168.0.3:9092 --describe --group groupName
# 生产消息
./kafka-console-producer.sh --broker-list 192.168.0.1:9092 --topic topicName
# 消费消息
./kafka-console-consumer.sh --bootstrap-server 192.168.0.1:9092 --topic topicName
# 从0消费所有消息
./kafka-console-consumer.sh --bootstrap-server 192.168.0.1:9092 --topic topicName --from-beginning

# ES导出指定索引中的数据
elasticdump --input http://192.168.0.1:9200/default_data --output ./default_data.json --type=data
# ES导入指定数据到索引中
elasticdump --input=/opt/default_data.json --output=http://192.168.0.1:9200/default_data
# 集群属性
GET _cluster/health    
# 查看所有的索引
curl http://192.168.0.1:9200/_cat/indices
# 获取文档指定id的数据
curl -XGET 'http://192.168.0.1:9200/default_index/_doc/123456?pretty=true'
GET default_index/_doc/123456
# 全量查询
GET default_index/_search
{
    "query": {
        "match_all": {}
    }
}
# 条件查询
GET default_index/_search
{
   "query": {
       "match": {
         "uid": 111
       }
   }
}
# 覆盖修改
PUT default_index/_doc/123456
{
    "id": 42,
    "name": "技师1",
    "price": 298
}
# 局部修改
POST default_index/_doc/123456
{
    "id": 66,
    "name": "技师2",
    "price": 398
}
# 清除索引内指定id的数据
DELETE default_index/_doc/123456
# 分析
GET _analyze
{
    "analyzer": "standard",
    "text" : "wu yi fan"
}
# 去除分页数量限制报错
curl -XPUT http://192.168.0.1:9200/default_index/_settings -d '{ "index" : { "max_result_window" : 100000000}}'
PUT default_index/_settings
{
    "index":{
    "max_result_window":1000000
}
# 全量查询分页及排序
GET default_index/_search
{
    "query": {
        "match": {
            "name": "技师2"
        }
    },
    "from":0,
    "size":1,
    "_source":["name"],
    "sort":{
        "id":{
            "order":"desc"
        }
    }
}
# 多条件查找
GET default_index/_search
{
    "query": {
        "bool":{
            "must":[
                {"match":{
                    "name":"技师2"
                    }
                },
                {"match":{
                    "id": 66
                    }
                }
            ],
            "filter":{
                "range":{
                    "price":{
                        "gt": 298
                    }
                }
            }
        }
    }
}
# 新增索引
curl -X PUT "http://192.168.0.1:9200/default_index" -H 'Content-Type: application/json' -d'
PUT default_index
{
  "mappings": {
    "_doc": {
      "properties": {
        "USER_ID": {
          "type": "long"
        },
        "someString": {
          "type": "text",
          "fields": {
            "keyword": {
              "type": "keyword",
              "ignore_above": 256
            }
          },
          "fielddata": true
        },
        "someShortName": {
          "type": "text",
          "analyzer": "ik_max_word"
        },
        "someType": {
          "type": "text",
          "analyzer": "ik_smart",
          "fielddata": true
        },
        "roomId": {
          "type": "integer"
        },
        "someIds": {
          "type": "text",
          "fields": {
            "keyword": {
              "type": "keyword",
              "ignore_above": 256
            }
          }
        },
        "somePrices": {
          "type": "nested",
          "properties": {
            "endDate": {
              "type": "date"
            },
            "sellPrice": {
              "type": "double"
            },
            "startDate": {
              "type": "date"
            }
          }
        },
        "isSale": {
          "type": "boolean"
        },
        "location": {
          "type": "geo_point"
        },
        "valid": {
          "type": "boolean"
        }
      }
    }
  }
}

# centos 基础
docker pull centos:7.9.2009
# 运行基础
docker run -d -it -p 8888:8888 -p 888:888 -p 80:80 -p 443:443 -p 3306:3306 --privileged=true centos:7.9.2009
# 查看当前运行
docker ps
# 进入容器
docker exec -it 容器ID bash
# 安装宝塔
yum install -y wget && wget -O install.sh https://download.bt.cn/install/install_6.0.sh && sh install.sh ed8484bec

# 容器转镜像
docker commit -m="centos_7.9_bt" -a="caoofduty" 1e625addbac9 caoofduty/centos_7.9_bt:pro

# 拉取生成镜像
docker pull caoofduty/centos_7.9_bt:pro
# 服务运行
docker run -d -it -p 8888:8888 -p 888:888 -p 80:80 -p 443:443 -p 3306:3306 -p 40465:40465 --privileged=true caoofduty/centos_7.9_bt:pro
# 查看当前运行
docker ps
# 进入容器
docker exec -it 容器ID bash
# 宝塔重启
bt restart

#其他:
外网面板地址: http://101.87.XXX.XXX:40465/a20130f1
内网面板地址: http://127.0.0.1:40465/a20130f1
username: xxxxx
password: xxxxx

# 拉取镜像
docker pull caoofduty/merchant_2023:dev
# 服务运行
docker run -d -it -p 8888:8888 -p 888:888 -p 80:80 -p 443:443 -p 3306:3306 -p 40465:40465 --privileged=true caoofduty/merchant_2023:dev
# 查看当前运行
docker ps
# 进入容器
docker exec -it 容器ID bash
# 宝塔重启
bt restart

# 其他:
外网面板地址: http://101.87.XXX.XXX:40465/a20130f1
内网面板地址: http://127.0.0.1:40465/a20130f1
username: xxxxx
password: xxxxx

# 装填: 
软件商店->开启各服务

# 输出
cd /www/wwwroot/xxx.merchant.cn
php think swoole restart &
php think queue:listen --tries 2 &

# 拉取镜像
docker pull caoofduty/hadoop-base:latest
# 搭建网络
docker network create --subnet=172.8.0.0/16 hadoopnetwork
# 集群战术部署
docker run -itd --name hadoop2  --net hadoopnetwork  --ip 172.8.0.2 --add-host hadoop2:172.8.0.2 --add-host hadoop3:172.8.0.3 --add-host hadoop4:172.8.0.4 -d -p 8088:8088 -p 9000:9000 -p 50070:50070 -p 9001:9001 -p 8030:8030 -p 8031:8031 -p 8032:8032 -p 8033:8033 -p 10020:10020 -p 19888:19888 caoofduty/hadoop-base:latest /bin/bash
docker run -itd --name hadoop3  --net hadoopnetwork  --ip 172.8.0.3 --add-host hadoop2:172.8.0.2 --add-host hadoop3:172.8.0.3 --add-host hadoop4:172.8.0.4 -d -P caoofduty/hadoop-base:latest /bin/bash
docker run -itd --name hadoop4  --net hadoopnetwork  --ip 172.8.0.4 --add-host hadoop2:172.8.0.2 --add-host hadoop3:172.8.0.3 --add-host hadoop4:172.8.0.4 -d -P caoofduty/hadoop-base:latest /bin/bash
# 开启容器的SSH
docker exec -d hadoop2 /usr/sbin/sshd
docker exec -d hadoop3 /usr/sbin/sshd
docker exec -d hadoop4 /usr/sbin/sshd
# 进入Master容器-刷新环境变量
source /etc/profile 
# 进入Master容器-启动集群
/usr/local/hadoop/sbin/start-all.sh
# 该镜像已集成后续的核心配置项,无需调整-开箱即用。
# 访问后台查看节点状态: http://localhost:8088/cluster

# 拉取镜像
docker pull caoofduty/centos-ssh
# 启动容器
docker run -itd --name centos-ssh caoofduty/centos-ssh /bin/bash
#查看ssh服务是否已开启
netstat -antp | grep sshd
# 开启SSH服务
/usr/sbin/sshd
# 设置免密登录
ssh-keygen -t rsa -P '' -f ~/.ssh/id_rsa
cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys

# 配置文件:core-site.xml
# 配置用途:Hadoop Core的配置项，例如HDFS和MapReduce常用的I/O设置等。
# 配置详情:
<?xml version="1.0" encoding="UTF-8"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<!--
  Licensed under the Apache License, Version 2.0 (the "License");
  you may not use this file except in compliance with the License.
  You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

  Unless required by applicable law or agreed to in writing, software
  distributed under the License is distributed on an "AS IS" BASIS,
  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  See the License for the specific language governing permissions and
  limitations under the License. See accompanying LICENSE file.
-->

<!-- Put site-specific property overrides in this file. -->

<configuration>
    <property>
        <name>fs.default.name</name>
        <value>hdfs://hadoop2/</value>
    </property>
    <property>
        <name>io.file.buffer.size</name>
        <value>131072</value>
    </property>
    <property>
        <name>hadoop.tmp.dir</name>
        <value>/home/hadoop/tmp</value>
        <description>Abase for other temporary directories.</description>
    </property>
</configuration>

# 配置文件:hdfs-site.xml
# 配置用途:HDFS功能配置等。
# 配置详情:
<?xml version="1.0" encoding="UTF-8"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<!--
  Licensed under the Apache License, Version 2.0 (the "License");
  you may not use this file except in compliance with the License.
  You may obtain a copy of the License at
    http://www.apache.org/licenses/LICENSE-2.0
  Unless required by applicable law or agreed to in writing, software
  distributed under the License is distributed on an "AS IS" BASIS,
  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  See the License for the specific language governing permissions and
  limitations under the License. See accompanying LICENSE file.
-->

<!-- Put site-specific property overrides in this file. -->

<configuration>
    <property>
        <name>dfs.namenode.secondary.http-address</name>
        <value>hadoop2:9001</value>
        <description># 通过web界面来查看HDFS状态 </description>
    </property>
    <property>
        <name>dfs.namenode.name.dir</name>
        <value>/home/hadoop/dfs/name</value>
    </property>
    <property>
        <name>dfs.datanode.data.dir</name>
        <value>/home/hadoop/dfs/data</value>
    </property>
    <property>
        <name>dfs.replication</name>
        <value>2</value>
        <description># 每个Block有2个备份</description>
    </property>
    <property>
        <name>dfs.webhdfs.enabled</name>
        <value>true</value>
    </property>
</configuration>

# 配置文件:mapred-site.xml
# 配置用途:MapReduce配置相关。
# 配置详情:
<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<!--
  Licensed under the Apache License, Version 2.0 (the "License");
  you may not use this file except in compliance with the License.
  You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

  Unless required by applicable law or agreed to in writing, software
  distributed under the License is distributed on an "AS IS" BASIS,
  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  See the License for the specific language governing permissions and
  limitations under the License. See accompanying LICENSE file.
-->

<!-- Put site-specific property overrides in this file. -->


<configuration>
    <property>
        <name>mapreduce.framework.name</name>
        <value>yarn</value>
    </property>
    <property>
        <name>mapreduce.jobhistory.address</name>
        <value>hadoop2:10020</value>
    </property>
    <property>
        <name>mapreduce.jobhistory.webapp.address</name>
        <value>hadoop2:19888</value>
    </property>
</configuration>

# 配置文件:yarn-site.xml
# 配置用途:yarn配置相关。
# 配置详情:
<?xml version="1.0"?>
<!--
  Licensed under the Apache License, Version 2.0 (the "License");
  you may not use this file except in compliance with the License.
  You may obtain a copy of the License at
    http://www.apache.org/licenses/LICENSE-2.0
  Unless required by applicable law or agreed to in writing, software
  distributed under the License is distributed on an "AS IS" BASIS,
  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  See the License for the specific language governing permissions and
  limitations under the License. See accompanying LICENSE file.
-->
<configuration>
    <!-- Site specific YARN configuration properties -->
    <property>
        <name>yarn.nodemanager.aux-services</name>
        <value>mapreduce_shuffle</value>
    </property>
    <property>
        <name>yarn.nodemanager.aux-services.mapreduce.shuffle.class</name>
        <value>org.apache.hadoop.mapred.ShuffleHandler</value>
    </property>
    <property>
        <name>yarn.resourcemanager.address</name>
        <value>hadoop2:8032</value>
    </property>
    <property>
        <name>yarn.resourcemanager.scheduler.address</name>
        <value>hadoop2:8030</value>
    </property>
    <property>
        <name>yarn.resourcemanager.resource-tracker.address</name>
        <value>hadoop2:8031</value>
    </property>
    <property>
        <name>yarn.resourcemanager.admin.address</name>
        <value>hadoop2:8033</value>
    </property>
    <property>
        <name>yarn.resourcemanager.webapp.address</name>
        <value>hadoop2:8088</value>
    </property>
    <property>
        <name>yarn.nodemanager.resource.memory-mb</name>
        <value>1024</value>
    </property>
    <property>
        <name>yarn.nodemanager.resource.cpu-vcores</name>
        <value>1</value>
    </property>
</configuration>

# 配置Hadoop的相关JAVA环境变量
vi /usr/local/hadoop/etc/hadoop/hadoop-env.sh
export JAVA_HOME=/usr/local/jdk1.8

vi /usr/local/hadoop/etc/hadoop/mapred-env.sh
export JAVA_HOME=/usr/local/jdk1.8

vi /usr/local/hadoop/etc/hadoop/yarn-env.sh
export JAVA_HOME=/usr/local/jdk1.8

# 同步命令(场景:以当前hadoop2为主,向hadoop3同步数据)
scp -rq /usr/local/hadoop hadoop3:/usr/local

# 初次运行-进入容器-对HDFS进行初始化
/usr/local/hadoop/bin/hdfs namenode -format

# scp命令传输报错丢链接(PING主机可正常通信的情况)
# 编辑ip登录白名单
vi /etc/hosts.allow
# 添加ip
sshd:172.8.0.2
sshd:172.8.0.3
sshd:172.8.0.4
# 重启sshd服务
service sshd restart
# 查看服务启动状态
service sshd status
# 注意:不同的服务器都要设置哦,如Hadoop2传输到Hadoop3,两台都需要。

# 启动报错
Starting namenodes on [hadoop2]
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 [hadoop2]
ERROR: Attempting to operate on hdfs secondarynamenode as root
ERROR: but there is no HDFS_SECONDARYNAMENODE_USER defined. Aborting operation.
2021-09-25 08:16:02,231 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Starting resourcemanager
ERROR: Attempting to operate on yarn resourcemanager as root
ERROR: but there is no YARN_RESOURCEMANAGER_USER defined. Aborting operation.
Starting nodemanagers
ERROR: Attempting to operate on yarn nodemanager as root
ERROR: but there is no YARN_NODEMANAGER_USER defined. Aborting operation.

# 编辑配置
vi /etc/profile
# 添加内容
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
# 立即生效
source /etc/profile 

# 拉取镜像
docker pull centos:7
# 启动容器  -i: 交互式操作。 -t: 终端。 -d: 在后台运行。 centos: centos镜像。 /bin/bash：放在镜像名后的是命令，这里我们希望有个交互式 Shell，因此用的是 /bin/bash。   
docker run -it centos /bin/bash
# 退出终端
exit
# 运行web应用  -d:让容器在后台运行。 -P:将容器内部使用的网络端口随机映射到我们使用的主机上。
docker run -d -P training/webapp python app.py
# 运行web应用指定端口
docker run -d -p 5000:5000 training/webapp python app.py
# 停止web应用容器
docker stop redis(CONTAINER)
# 查看运行的容器
docker ps
# 查看web应用日志(docker logs可以像使用 tail -f 一样来输出容器内部的标准输出)
docker logs -f <CONTAINER_ID>(容器ID)
# 查看应用容器进程
docker top redis(CONTAINER)

# 查看所有容器
docker ps -a
# 启动已停止运行的容器
docker start <CONTAINER_ID>
# 停止一个容器
docker stop <CONTAINER_ID>
# 进入容器
docker attach <CONTAINER_ID>   输入exit从容器退出后，容器会停止。
docker exec -it <CONTAINER_ID> 输入exit从容器退出后，容器不会停止。
# 导出容器
docker export <CONTAINER_ID> > centos.tar  
# 删除容器
docker rm -f <CONTAINER_ID>

# 列出镜像列表    说明:REPOSITORY：表示镜像的仓库源。 TAG：镜像的标签。 IMAGE ID：镜像ID。 CREATED：镜像创建时间。 SIZE：镜像大小。
docker images 
# 查找镜像       说明:NAME: 镜像仓库源的名称。 DESCRIPTION: 镜像的描述。 OFFICIAL: 是否 docker 官方发布。 stars: 类似 Github 里面的 star，表示点赞、喜欢的意思。 AUTOMATED: 自动构建。
docker search hello-world
# 删除镜像 
docker rmi hello-world
# 创建镜像    1、从已经创建的容器中更新镜像，并且提交这个镜像 2、使用 Dockerfile 指令来创建一个新的镜像  说明：-m: 提交的描述信息。 -a: 指定镜像作者。 e218edb10551：容器 ID。 lixiang707/haha:v2: 指定要创建的目标镜像名。
docker commit -m="MOSS INIT" -a="caoofduty" e218edb10551 caoofduty/moss:v1
# 构建镜像   -t ：指定要创建的目标镜像名。  . ：Dockerfile 文件所在目录，可以指定Dockerfile 的绝对路径。
cat Dockerfile 
docker build -t lixiang707/centos:6.7 .
 # 本地保存镜像
docker save -o moss.tar caoofduty/moss:v1
# 设置镜像标签
docker tag 860c279d2aaa lixiang707/haha:dev
  

# 容器打包成镜像
docker commit deepseek-server deepseek-server:dev
# 镜像打标
docker tag deepseek-server:dev caoofduty/deepseek-server:dev
# 镜像推送到远程
docker push caoofduty/deepseek-server:dev