https://docs.docker.com/
Docker概述
Docker为什么会出现
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开发和运维的矛盾：“在我的电脑可以运行”
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从开发到上线，需要两套环境，不同环境都需要配置；
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应用版本更新，导致服务不可用，对运维提出挑战；
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环境配置非常麻烦：
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每一台机器都要配置环境（集群 Redis, ES, Hadoop）；
Docker的解决方案：
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发布一个项目【jar】项目包含了所有依赖环境【Redis，mysql， jdk, ES】;
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开发打包部署上线，一套流程做完，开发即运维；
apk - 发布（应用商店）- 下载、安装即可用；
jar 打包项目带上环境（镜像） - 发布（Docker仓库:商店） - 下载发布的镜像，直接可用；
Docker镜像逐渐成为了企业的交付标准
Docker的历史
2010年， 几个搞IT的年轻人， 在美国成立了一家公司 dotCloud，做一些Pass的云计算服务；
他们将自己的容器化技术命名为Docker ，Docker刚诞生的时候，没有引起行业的注意， dotCloud活不下去了就开源；
2013年，Docker开源了， 越来越多的人发现了docker的优点，Docker每个月都会更新一个版本；
2014年4月9日，Docker1.0发布；
Docker为什么这么火，因为十分轻巧， 在容器技术出来之前，都是使用的虚拟机技术；
虚拟机：在windows中使用Vmare， 通过这个软件可以虚拟出一台或者多台电脑，但是非常笨重；
虚拟机也是属于虚拟化技术，Docker使用容器技术，也是一种虚拟化技术；
Docker传送门
Docker是基于Go语言开发的
官方网站
https://www.docker.com/
官方文档
https://docs.docker.com/?_gl=1*18f27x1*_ga*MTUwNjQxMjE1OC4xNjg2NzMyODU1*_ga_XJWPQMJYHQ*MTY5MjE3MzUyNS41LjEuMTY5MjE3Mzk2Mi42MC4wLjA.
Docker Hub
https://hub.docker.com/
Docker能干嘛
虚拟机技术
虚拟机技术的缺点：
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资源占用十分多；
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冗余步骤多；
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启动很慢
容器化技术
容器化技术不是模拟的一个完整的操作系统
比较Docker和虚拟机技术的不同
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传统虚拟机，运行一个完整的操作系统，然后在这个系统上安装和运行软件；
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容器内的应用直接运行在宿主机的内核，容器是没有自己的内核，也没有虚拟硬件，所以就轻便；
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每个容器间是互相隔离，每个容器内都有一个属于自己的文件系统，互不影响；
DevOps(开发、运维)
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应用更快速的交付和部署
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传统：一堆帮助文档和安装程序；
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Docker： 打包镜像发布测试，一键运行；
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更便捷的升级和扩缩容
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使用了docker之后，我们部署应用就和搭积木一样；
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项目打包为一个镜像，做扩展的时候，直接部署到服务器A, B , C…；
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更简单的系统运维
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在容器化后，我们的开发，测试环境都是高度一致的；
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更高效的计算资源利用
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Docker是内核级别的虚拟化，可以在一个物理机上运行很多的容器实例，服务器性能可以被压榨到极致；
Docker安装
Docker的基本组成
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客户端
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服务器
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远程仓库
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镜像（image）:
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docker镜像就好比是一个模板，可以通过这个模板来创建容器服务;
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tomcat镜像 => run => tomcat01容器，提供服务，通过这个镜像可以创建多个容器；
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最终服务运行或者项目运行就是在容器中的；
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容器（container）:
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Docker利用容器技术，独立运行一个或者一组应用，通过镜像来创建；
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容器可以启动，停止，删除；
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可以把容器理解为一个简易的linux系统
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仓库（repository）：
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仓库就是存放镜像的地方；
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仓库分为共有仓库和私有仓库；
环境查看
root@dev:~ # cat /etc/os-releaseNAME="Ubuntu"VERSION="16.04.7 LTS (Xenial Xerus)"ID=ubuntuID_LIKE=debianPRETTY_NAME="Ubuntu 16.04.7 LTS"VERSION_ID="16.04"HOME_URL="http://www.ubuntu.com/"SUPPORT_URL="http://help.ubuntu.com/"BUG_REPORT_URL="http://bugs.launchpad.net/ubuntu/"VERSION_CODENAME=xenialUBUNTU_CODENAME=xenial
官方安装路径
https://docs.docker.com/engine/install/ubuntu/
docker-ce：社区版本
docker-ee：企业版（没啥用）
root@dev:~ # docker versionClient:Version: 18.03.0-ceAPI version: 1.37Go version: go1.9.4Git commit: 0520e24Built: Wed Mar 21 23:10:01 2018OS/Arch: linux/amd64Experimental: falseOrchestrator: swarmServer:Engine:Version: 18.03.0-ceAPI version: 1.37 (minimum version 1.12)Go version: go1.9.4Git commit: 0520e24Built: Wed Mar 21 23:08:31 2018OS/Arch: linux/amd64Experimental: false
HelloWorld
底层原理
Docker是怎么工作的
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Docker是一个Client-Server结构的系统， Docker的守护进程运行在主机上，通过Socket从客户端访问；
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DockerServer接收到Docker-Client的命令，就会执行这个命令；
Docker为什么比VM快
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Docker有比虚拟机更少的抽象层
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Docker利用的是宿主机的内核，VM需要是Guest OS
所以说，新建一个容器的时候，docker不需要像虚拟机一样重新加载一个操作系统内核，避免引导；
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虚拟机是加载Guest OS，需要分钟级别启动；
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docker利用宿主机的操作系统，省略了复杂的过程，秒级启动；
Docker 常用命令
帮助命令
docker version # 显示docker的版本信息docker info # 显示docker的系统信息， 包括镜像和容器的数量docker 命令 --help # 万能命令
帮助文档的地址：
https://docs.docker.com/reference/
镜像命令
docker images ：查看镜像
查看所有本地主机上的镜像
root@dev:~ # docker imagesREPOSITORY TAG IMAGE ID CREATED SIZEtensorflow/serving latest 97e0a2470728 3 weeks ago 518MBtensorflow/serving <none> 07511a317c0b 3 months ago 510MBtensorflow/serving <none> e874bf5e4700 2 years ago 406MBhello-world latest d1165f221234 2 years ago 13.3kB
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REPOSITORY 镜像的仓库源
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TAG 镜像的标签
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IMAGE ID 镜像的ID
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CREATE 镜像的创建时间
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SIZE 镜像的大小
Usage: docker images [OPTIONS] [REPOSITORY[:TAG]]List imagesOptions:-a, --all Show all images (default hides intermediate images)--digests Show digests-f, --filter filter Filter output based on conditions provided--format string Pretty-print images using a Go template--no-trunc Don't truncate output-q, --quiet Only show numeric IDs
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-a 列出所有的镜像
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-q 只显示镜像的id
root@dev:~ # docker images -aq97e0a247072807511a317c0be874bf5e4700d1165f221234
docker search：搜索镜像
root@dev:~ # docker search mysqlNAME DESCRIPTION STARS OFFICIAL AUTOMATEDmysql MySQL is a widely used, open-source relation… 14384mariadb MariaDB Server is a high performing open sou… 5490phpmyadmin phpMyAdmin - A web interface for MySQL and M… 849percona Percona Server is a fork of the MySQL relati… 620bitnami/mysql Bitnami MySQL Docker Image 93
可选项， 通过收藏来过滤
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--filter-STARS=3000, 搜索出来的镜像是收藏大于3000
root@dev:~ # docker search mysql --filter=STARS=3000NAME DESCRIPTION STARS OFFICIAL AUTOMATEDmysql MySQL is a widely used, open-source relation… 14384 [OK]mariadb MariaDB Server is a high performing open sou… 5490 [OK]
docker pull：下载镜像
# 下载镜像 docker pull 镜像名[:tag]
如果不写tag， 默认就是latest
docker下载的时候是分层下载（相关的知识点：联合文件系统）
root@dev:~ # docker imagesREPOSITORY TAG IMAGE ID CREATED SIZEmysql latest 99afc808f15b 5 days ago 577MBtensorflow/serving latest 97e0a2470728 3 weeks ago 518MBtensorflow/serving <none> 07511a317c0b 3 months ago 510MBtensorflow/serving <none> e874bf5e4700 2 years ago 406MBhello-world latest d1165f221234 2 years ago 13.3kB
指定版本的镜像
root@dev:~ # docker pull mysql:5.75.7: Pulling from library/mysql70e9ff4420fb: Pull complete7ca4383b183f: Pull complete3e282e7651b1: Pull complete1ffa0e0ca707: Pull complete6eb790cf6382: Pull completeb4b277ff2929: Pull complete692fe4469429: Pull completec0d447d97bbd: Pull complete99ee594517ba: Pull completea9ae52de4d77: Pull complete66cc05a182b5: Pull completeDigest: sha256:2c23f254c6b9444ecda9ba36051a9800e8934a2f5828ecc8730531db8142af83Status: Downloaded newer image for mysql:5.7root@dev:~ # docker imagesREPOSITORY TAG IMAGE ID CREATED SIZEmysql latest 99afc808f15b 5 days ago 577MBmysql 5.7 92034fe9a41f 13 days ago 581MBtensorflow/serving latest 97e0a2470728 3 weeks ago 518MBtensorflow/serving <none> 07511a317c0b 3 months ago 510MBtensorflow/serving <none> e874bf5e4700 2 years ago 406MBhello-world latest d1165f221234 2 years ago 13.3kB
docker rmi：删除镜像
删除镜像， 可以通过镜像的ID也可以通过镜像的名称删除
root@dev:~ # docker rmi mysqlUntagged: mysql:latestUntagged: mysql@sha256:c0455ac041844b5e65cd08571387fa5b50ab2a6179557fd938298cab13acf0ddDeleted: sha256:99afc808f15be15cd1d9890394123034a79d1f3204d5b417d8558990cdf30167Deleted: sha256:4ef558dbdf981f0511214063c2f024fdc349bdfc096e1c0c5fe051df7715d3a8Deleted: sha256:2c65961b0dc1248756315521ec79f56d5baaa6c2dde56630327f13088bdb8c48Deleted: sha256:ef0d0e39979a8108231a1ef7b79419a76ec3b3bd4d4604b6b17d132ab39fe0dbDeleted: sha256:9cf001ade3f88ba49e7c9a3cdf74deb7dc00256921b7b90911d8db389c3b91a3Deleted: sha256:649167b3b6547c68b0327f5eed76a5b8e1aab76a040748b16ff693c3f55d52adDeleted: sha256:e42aec727a07fdc663fcf11eab94a9e8357b9242ba278190544c1c42c1f7ed93Deleted: sha256:c91f3694a10505a6c50a83511d3ae43c45858019d267c1b002bcd1b8295c9896Deleted: sha256:7af435fc0f64d278f0567aaf575001c9952e5ed5f69d72f049c15a9e5a5b9d62Deleted: sha256:8f5cb0ae989a7a9dd9ec73a0b48fa0c5fe9691b731f74a559ed043cf37ab2aa7Deleted: sha256:3bf99074f96df6af6ec205f8df88af239fc48ff936c75e3af9a6a1626f22a567
root@dev:~ # docker imagesREPOSITORY TAG IMAGE ID CREATED SIZEmysql 5.7 92034fe9a41f 13 days ago 581MBtensorflow/serving latest 97e0a2470728 3 weeks ago 518MBtensorflow/serving <none> 07511a317c0b 3 months ago 510MBtensorflow/serving <none> e874bf5e4700 2 years ago 406MBhello-world latest d1165f221234 2 years ago 13.3kB
root@dev:~ # docker rmi 92034fe9a41fUntagged: mysql:5.7Untagged: mysql@sha256:2c23f254c6b9444ecda9ba36051a9800e8934a2f5828ecc8730531db8142af83Deleted: sha256:92034fe9a41f4344b97f3fc88a8796248e2cfa9b934be58379f3dbc150d07d9dDeleted: sha256:df9054037a39d9d0e98b43bbf940793c0b5bdcfa165a2895ee87b4c1dc8f362fDeleted: sha256:8eb2c1c2676cb3cb3d1e01fada914df102cddddcd5763dce1a623b9505882f2dDeleted: sha256:c38117f45694b767c799d7a4e3941e69f6bd05db6cbb5373126eb29842661e19Deleted: sha256:1cf26960e390f7df8be8f0f4d23c12441b832e95ededa363c9e2eb1be4dba1bbDeleted: sha256:c646c79ec9cf7480a2f1fc40ebda6ce1973571f3e166015f36cc7bff29d3ef2cDeleted: sha256:13a81468adbe0652a713fea05ce7cfa6d4326f1560cd77b3b468fc9721e8a5d2Deleted: sha256:422c4721f357ead3754ea1b3a630d5334a93db6a5d3d41642778e50194e5e9b4Deleted: sha256:3356622c341e34d779294e5c450424a43bd6f55538a1dbbbdd30489f33315764Deleted: sha256:10213d78b510219f6cf8e143c99d29db6ba8dc6bea318ae9c812ea5948805b39Deleted: sha256:82afa988bdb62c2f462d0d88e709afaf7b529cc944ea5197633c875e2a70636fDeleted: sha256:616461b0543d6905f05f2b384bc403d268886c8845a6de09629a2b022388c830
root@dev:~ # docker imagesREPOSITORY TAG IMAGE ID CREATED SIZEtensorflow/serving latest 97e0a2470728 3 weeks ago 518MBtensorflow/serving <none> 07511a317c0b 3 months ago 510MBtensorflow/serving <none> e874bf5e4700 2 years ago 406MBhello-world latest d1165f221234 2 years ago 13.3kB
删除镜像
docker rmi -f $(docker images -aq) # 删除所有镜像docker rmi -f 镜像id # 删除指定镜像docker rmi -f 镜像id—1 镜像id-2 镜像id-3 #删除多个镜像
容器命令
说明：我们有了镜像才可以创建容器， 下载一个centos镜像来测试学习
root@dev:~ # docker pull centosUsing default tag: latestlatest: Pulling from library/centosa1d0c7532777: Pull completeDigest: sha256:a27fd8080b517143cbbbab9dfb7c8571c40d67d534bbdee55bd6c473f432b177Status: Downloaded newer image for centos:latestroot@dev:~ # docker imagesREPOSITORY TAG IMAGE ID CREATED SIZEtensorflow/serving latest 97e0a2470728 3 weeks ago 518MBtensorflow/serving <none> 07511a317c0b 3 months ago 510MBcentos latest 5d0da3dc9764 23 months ago 231MBtensorflow/serving <none> e874bf5e4700 2 years ago 406MBhello-world latest d1165f221234 2 years ago 13.3kB
docker run：新建容器并启动
docker run [可选参数] image# 参数说明--name="Name" 容器名字，tomcat01, tomcat02, 用来区分容器-d 后台方式运行-it 使用交互方式运行，进入容器查看内容-p 置顶容器的端口， -p 8080:8080-p 主机端口：容器端口-p 容器端口-p ip：主机端口：容器端口-P 随机指定端口-v 数据卷挂载， 可以用多个-e 环境配置
root@dev:~ # docker run -it centos /bin/bash# 主机名其实就是镜像ID[root@315989e8155c /]# lsbin dev etc home lib lib64 lost+found media mnt opt proc root run sbin srv sys tmp usr var# 从容器中退回主机[root@315989e8155c /]# exitexitroot@dev:~ # lsanaconda3 install.jenkins.sh logtail.sh start.server.sh tomcat.ab.origin.log tomcat.ab.threads.logdangxuan install.jenkinsuser.sh NVIDIA_CUDA-11.2_Samples temp.txt tomcat.ab.t117.log xuzijungame-python jupyter.log recommend-python tomcat.ab.log tomcat.ab.threads2.log
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run -d : 后台启动
docker run -d centos# 问题docker ps 发现centos停止了# 常见的坑， docker使用后台运行，就必须要有一个前台进程，docker发现没有应用，就会自动停止
docker ps：列出运行的容器
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docker ps ： 列出当前正在运行的容器，如果容器停了就不会展现
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docker ps -a ： 查看曾经运行过的容器
# docker ps 列出当前正在运行的容器-a # 列出当前正在运行的容器，带出历史运行过的容器-n=? # 显示出最近创建的容器-q # 只显示容器的编号
exit: 退出容器
exit #直接容器停止并退出Ctrl + P + Q #容器不停止退出
docker rm : 删除容器
docker rm 容器id # 删除指定的容器docker rm $(docker ps -aq) # 删除所有的容器docker ps -a -q | xargs docker rm # 删除所有的容器
不能删除正在运行的容器，如果要强制删除，rm -f
root@dev:~ # docker psCONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES7242caa68ff1 centos "/bin/bash" 3 minutes ago Up 3 minutes infallible_clarke07ef5d12ab92 tensorflow/serving "/usr/bin/tf_serving…" 3 hours ago Up 3 hours 8500/tcp, 0.0.0.0:8501->8501/tcp clever_kirchroot@dev:~ # docker rm 7242caa68ff1Error response from daemon: You cannot remove a running container 7242caa68ff1cc012cdfc1786b7fd94220aaacdf0773c85c1f62125b8ea3bde8. Stop the container before attempting removal or force remove
root@dev:~ # docker rm -f 7242caa68ff17242caa68ff1root@dev:~ # docker psCONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES07ef5d12ab92 tensorflow/serving "/usr/bin/tf_serving…" 3 hours ago Up 3 hours 8500/tcp, 0.0.0.0:8501->8501/tcp clever_kirch
docker start & stop : 启动和停止容器的操作
docker start 容器id # 启动容器docker restart 容器id # 重启容器docker stop 容器id # 停止当前正在运行的容器docker kill 容器id # 杀掉容器
root@dev:~ # docker psCONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES07ef5d12ab92 tensorflow/serving "/usr/bin/tf_serving…" 3 hours ago Up 3 hours 8500/tcp, 0.0.0.0:8501->8501/tcp clever_kirch
root@dev:~ # docker start e6ee99288a9ee6ee99288a9eroot@dev:~ # docker psCONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMESe6ee99288a9e centos "/bin/bash" 19 seconds ago Up 3 seconds gracious_nightingale07ef5d12ab92 tensorflow/serving "/usr/bin/tf_serving…" 3 hours ago Up 3 hours 8500/tcp, 0.0.0.0:8501->8501/tcp clever_kirch
root@dev:~ # docker psCONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMESe6ee99288a9e centos "/bin/bash" About a minute ago Up About a minute gracious_nightingale07ef5d12ab92 tensorflow/serving "/usr/bin/tf_serving…" 3 hours ago Up 3 hours 8500/tcp, 0.0.0.0:8501->8501/tcp clever_kirch
root@dev:~ # docker stop e6ee99288a9ee6ee99288a9eroot@dev:~ # docker psCONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES07ef5d12ab92 tensorflow/serving "/usr/bin/tf_serving…" 3 hours ago Up 3 hours 8500/tcp, 0.0.0.0:8501->8501/tcp clever_kirch
docker exec： 进入当前正在运行的容器
我们容器通常都是使用后台方式运行的，需要进入容器，修改一些配置
# docker exec -it 容器idroot@dev:~ # docker exec -it 0850b7f88891 /bin/bash[root@0850b7f88891 /]# ps -efUID PID PPID C STIME TTY TIME CMDroot 1 0 0 03:06 ? 00:00:00 /bin/sh -c while true;do echo maroot 859 0 0 03:20 pts/0 00:00:00 /bin/bashroot 875 1 0 03:20 ? 00:00:00 /usr/bin/coreutils --coreutils-proot 876 859 0 03:20 pts/0 00:00:00 ps -ef[root@0850b7f88891 /]# exitexit
对比一下docker run 就是新建一个容器
root@dev:~ # docker psCONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES0850b7f88891 centos "/bin/sh -c 'while t…" 10 minutes ago Up 10 minutes unruffled_jepsen07ef5d12ab92 tensorflow/serving "/usr/bin/tf_serving…" 3 hours ago Up 3 hours 8500/tcp, 0.0.0.0:8501->8501/tcp clever_kirch
root@dev:~ # docker run -it centos[root@fd7f20ee8d6e /]# #root@dev:~ # docker psCONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMESfd7f20ee8d6e centos "/bin/bash" 14 seconds ago Up 13 seconds cocky_swartz0850b7f88891 centos "/bin/sh -c 'while t…" 11 minutes ago Up 11 minutes unruffled_jepsen07ef5d12ab92 tensorflow/serving "/usr/bin/tf_serving…" 3 hours ago Up 3 hours 8500/tcp, 0.0.0.0:8501->8501/tcp clever_kirch
docker attach : 也是进入当前正在运行的容器
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docker exec # 进入容器后开启一个新的终端， 可以在里面操作（常用）
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docker attach # 进入容器正在执行的终端，不会启动新的进程；
root@dev:~ # docker psCONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES0850b7f88891 centos "/bin/sh -c 'while t…" 15 minutes ago Up 15 minutes unruffled_jepsen07ef5d12ab92 tensorflow/serving "/usr/bin/tf_serving…" 3 hours ago Up 3 hours 8500/tcp, 0.0.0.0:8501->8501/tcp clever_kirch
docker logs: 查看日志
root@dev:~ # docker logs -f -t --tail 10 e6ee99288a9e2023-08-17T02:55:08.917130283Z [root@e6ee99288a9e /]# exit2023-08-17T02:55:08.917202631Z exit2023-08-17T02:56:37.405344334Z [root@e6ee99288a9e /]# exit#显示日志-tf #显示日志，-t是会带上时间戳--tail number #要显示日志条数root@dev:~ # docker run -d centos /bin/sh -c "while true;do echo maoyaozong;sleep 1;done"0850b7f888917782634b1f75d04b92bb3fb335829296c13b1a7cd8b1b0cf664a
docker top: 查看容器中的进程信息
root@dev:~ # docker top 0850b7f88891UID PID PPID C STIME TTY TIME CMDroot 14933 14916 0 11:06 ? 00:00:00 /bin/sh -c while true;do echo maoyaozong;sleep 1;doneroot 15378 14933 0 11:10 ? 00:00:00 /usr/bin/coreutils --coreutils-prog-shebang=sleep /usr/bin/sleep 1
docker inspect: 查看容器元信息
root@dev:~[{"Id": "0850b7f888917782634b1f75d04b92bb3fb335829296c13b1a7cd8b1b0cf664a","Created": "2023-08-17T03:06:43.299459431Z","Path": "/bin/sh","Args": ["-c","while true;do echo maoyaozong;sleep 1;done"],"State": {"Status": "running","Running": true,"Paused": false,"Restarting": false,"OOMKilled": false,"Dead": false,"Pid": 14933,"ExitCode": 0,"Error": "","StartedAt": "2023-08-17T03:06:43.653879501Z","FinishedAt": "0001-01-01T00:00:00Z"},"Image": "sha256:5d0da3dc976460b72c77d94c8a1ad043720b0416bfc16c52c45d4847e53fadb6","ResolvConfPath": "/var/lib/docker/containers/0850b7f888917782634b1f75d04b92bb3fb335829296c13b1a7cd8b1b0cf664a/resolv.conf","HostnamePath": "/var/lib/docker/containers/0850b7f888917782634b1f75d04b92bb3fb335829296c13b1a7cd8b1b0cf664a/hostname","HostsPath": "/var/lib/docker/containers/0850b7f888917782634b1f75d04b92bb3fb335829296c13b1a7cd8b1b0cf664a/hosts","LogPath": "/var/lib/docker/containers/0850b7f888917782634b1f75d04b92bb3fb335829296c13b1a7cd8b1b0cf664a/0850b7f888917782634b1f75d04b92bb3fb335829296c13b1a7cd8b1b0cf664a-json.log","Name": "/unruffled_jepsen","RestartCount": 0,"Driver": "overlay2","Platform": "linux","MountLabel": "","ProcessLabel": "","AppArmorProfile": "docker-default","ExecIDs": null,"HostConfig": {"Binds": null,"ContainerIDFile": "","LogConfig": {"Type": "json-file","Config": {}},"NetworkMode": "default","PortBindings": {},"RestartPolicy": {"Name": "no","MaximumRetryCount": 0},"AutoRemove": false,"VolumeDriver": "","VolumesFrom": null,"CapAdd": null,"CapDrop": null,"Dns": [],"DnsOptions": [],"DnsSearch": [],"ExtraHosts": null,"GroupAdd": null,"IpcMode": "shareable","Cgroup": "","Links": null,"OomScoreAdj": 0,"PidMode": "","Privileged": false,"PublishAllPorts": false,"ReadonlyRootfs": false,"SecurityOpt": null,"UTSMode": "","UsernsMode": "","ShmSize": 67108864,"Runtime": "runc","ConsoleSize": [0,0],"Isolation": "","CpuShares": 0,"Memory": 0,"NanoCpus": 0,"CgroupParent": "","BlkioWeight": 0,"BlkioWeightDevice": [],"BlkioDeviceReadBps": null,"BlkioDeviceWriteBps": null,"BlkioDeviceReadIOps": null,"BlkioDeviceWriteIOps": null,"CpuPeriod": 0,"CpuQuota": 0,"CpuRealtimePeriod": 0,"CpuRealtimeRuntime": 0,"CpusetCpus": "","CpusetMems": "","Devices": [],"DeviceCgroupRules": null,"DiskQuota": 0,"KernelMemory": 0,"MemoryReservation": 0,"MemorySwap": 0,"MemorySwappiness": null,"OomKillDisable": false,"PidsLimit": 0,"Ulimits": null,"CpuCount": 0,"CpuPercent": 0,"IOMaximumIOps": 0,"IOMaximumBandwidth": 0},"GraphDriver": {"Data": {"LowerDir": "/var/lib/docker/overlay2/67f004b6510be2bcb8051ebbad966f1292f73531220b422f1b8bd7a959145dba-init/diff:/var/lib/docker/overlay2/e982c1c95f5cf3e928849023b4c5c02bd0f4018255a8c026754e60e31a078fb2/diff","MergedDir": "/var/lib/docker/overlay2/67f004b6510be2bcb8051ebbad966f1292f73531220b422f1b8bd7a959145dba/merged","UpperDir": "/var/lib/docker/overlay2/67f004b6510be2bcb8051ebbad966f1292f73531220b422f1b8bd7a959145dba/diff","WorkDir": "/var/lib/docker/overlay2/67f004b6510be2bcb8051ebbad966f1292f73531220b422f1b8bd7a959145dba/work"},"Name": "overlay2"},"Mounts": [],"Config": {"Hostname": "0850b7f88891","Domainname": "","User": "","AttachStdin": false,"AttachStdout": false,"AttachStderr": false,"Tty": false,"OpenStdin": false,"StdinOnce": false,"Env": ["PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin"],"Cmd": ["/bin/sh","-c","while true;do echo maoyaozong;sleep 1;done"],"Image": "centos","Volumes": null,"WorkingDir": "","Entrypoint": null,"OnBuild": null,"Labels": {"org.label-schema.build-date": "20210915","org.label-schema.license": "GPLv2","org.label-schema.name": "CentOS Base Image","org.label-schema.schema-version": "1.0","org.label-schema.vendor": "CentOS"}},"NetworkSettings": {"Bridge": "","SandboxID": "b3e217704a8919d20b1a32c30779921e62d78952cce831659ab09bd990adefe3","HairpinMode": false,"LinkLocalIPv6Address": "","LinkLocalIPv6PrefixLen": 0,"Ports": {},"SandboxKey": "/var/run/docker/netns/b3e217704a89","SecondaryIPAddresses": null,"SecondaryIPv6Addresses": null,"EndpointID": "f3edea89d0d3b80acf01f659c1c764bb6b678f742baaece058ff792111cd9df9","Gateway": "172.17.0.1","GlobalIPv6Address": "","GlobalIPv6PrefixLen": 0,"IPAddress": "172.17.0.3","IPPrefixLen": 16,"IPv6Gateway": "","MacAddress": "02:42:ac:11:00:03","Networks": {"bridge": {"IPAMConfig": null,"Links": null,"Aliases": null,"NetworkID": "915022882edaeda987987002b1d310cace261b012c201c4e9b245639e1cc7746","EndpointID": "f3edea89d0d3b80acf01f659c1c764bb6b678f742baaece058ff792111cd9df9","Gateway": "172.17.0.1","IPAddress": "172.17.0.3","IPPrefixLen": 16,"IPv6Gateway": "","GlobalIPv6Address": "","GlobalIPv6PrefixLen": 0,"MacAddress": "02:42:ac:11:00:03","DriverOpts": null}}}}]
docker cp : 从容器内拷贝文件到主机上
docker cp 容器id:容器内路径 目的地主机路径拷贝是一个手动过程， 更好的是通过-v 数据卷的方式进行打通
![Docker 容器技术]()
Docker部署实例
部署Nginx
root@dev:~ # docker search nginxroot@dev:~ # docker pull nginxroot@dev:~ # docker imagesREPOSITORY TAG IMAGE ID CREATED SIZEnginx latest eea7b3dcba7e 18 hours ago 187MBtensorflow/serving latest 97e0a2470728 3 weeks ago 518MBtensorflow/serving <none> 07511a317c0b 3 months ago 510MBcentos latest 5d0da3dc9764 23 months ago 231MBroot@dev:~ # docker run -d --name nginx01 -p 3340:80 nginxd9acd4cf581880f002490eb8b9864535d89c2c4d05d20b2d4f7a5807770b360aroot@dev:~ # docker psCONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMESd9acd4cf5818 nginx "/docker-entrypoint.…" 9 seconds ago Up 7 seconds 0.0.0.0:3340->80/tcp nginx0107ef5d12ab92 tensorflow/serving "/usr/bin/tf_serving…" 4 hours ago Up 4 hours 8500/tcp, 0.0.0.0:8501->8501/tcp clever_kirchroot@dev:~ # curl localhost:3340<html><head><title>Welcome to nginx!</title><style>html { color-scheme: light dark; }body { width: 35em; margin: 0 auto;font-family: Tahoma, Verdana, Arial, sans-serif; }</style></head><body><h1>Welcome to nginx!</h1><p>If you see this page, the nginx web server is successfully installed andworking. Further configuration is required.</p><p>For online documentation and support please refer to<ahref="http://nginx.org/">nginx.org</a>.<br/>Commercial support is available at<ahref="http://nginx.com/">nginx.com</a>.</p><p><em>Thank you for using nginx.</em></p></body></html>
Docker 可视化
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portainer
docker run -d -p 8088:9000 --restart=always -v /var/run/docker.sock:/var/run/docker.sock --privileged=true portainer/portainerDocker图形化界面管理工具，提供一个后台面板操作
https://docs.portainer.io/
Docker镜像
镜像是什么
镜像是一种轻量级、可执行的独立软件包，用来打包软件运行环境和基于运行环境开发的软件，他包含运行某个软件所需的所有内容，包括代码、运行时、库、环境变量和配置文件。
Docker镜像加载原理
UnionFS(联合文件系统)
UnionFS ( 联合文件系统): 是一种分层、轻量级并且高性能的文件系统，它支持对文件系统的修改作为一次提交来一层层的叠加，同时可以将不同目录挂载到同一个虚拟文件系统下。Union 文件系统是 Docker 像的基础。镜像可以通过分层来进行继承。
特性：一次同时加载多个文件系统，但从外面看起来，只能看到一个文件系统，联合加载会把各层文件系统叠加起来，这样最终的文件系统会包含所有底层的文件和目录
Docker镜像加载原理
docker的镜像实际上由一层一层的文件系统组成，这种层级的文件系统UnionFS。
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bootfs(boot file system)主要包含bootloader和kernel, bootloader主要是引导加载kernel, Linux刚启动时会加载bootfs文件系统，在Dockeri像的最底层是bootfs。这一层与我们典型的Linux/nix系统是一样的，包含boot加载器和内核。当boot加载完成之后整个内核就都在内存中了，此时内存的使用权已由bootfs转交给内核，此时系统也会卸载bootfs。
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rootfs (root file system), 在bootfs之上包含的就是典型 Linux 系统中的 /dev,/proc,/bin,/etc 等标准目录和文件。rootfs就是各种不同的操作系统发行版，比如Ubuntu，Centos等等。
平时我们安装进虚拟机的CentoS都是好几个G，为什么Docker这里才200M ?
对于一个精简的OS，rootfs 可以很小，只需要包含最基本的命令，工具和程序库就可以了，因为底层直接用Host的kernel，自己只需要提供rootfs就可以了。由此可见对于不同的linux发行版,bootfs基本是一致的,rotfs会有差别,因此不同的发行版可以公用bootfs。
Docker 分层理解
我们下载一个镜像， 注意观察下载的日志输出，可以看到是一层一层的在下载，已经下载过的就不会再下载了
思考: 为什么Docker镜像要采用这种分层的结构呢
最大的好处，我觉得莫过于是资源共享了! 比如有多个镜像都从相同的Base像构建而来，那么宿主机只需在磁盘上保留一份base镜像，同时内存中也只需要加载一份base镜像，这样就可以为所有的容器服务了，而且镜像的每一层都可以被共享。
查看镜像分层的方式可以通过 docker image inspect 命令!
root:~ # docker image inspect nginx[{"Id": "sha256:eea7b3dcba7ee47c0d16a60cc85d2b977d166be3960541991f3e6294d795ed24","RepoTags": ["nginx:latest"],"RepoDigests": ["nginx@sha256:13d22ec63300e16014d4a42aed735207a8b33c223cff19627dd3042e5a10a3a0"],"Parent": "","Comment": "","Created": "2023-08-16T09:50:55.765544033Z","Container": "50b019921f82064e1d8af7e2723929d4c5fafcfd6d8b03595711bd1e455dd3c4","ContainerConfig": {"Hostname": "50b019921f82","Domainname": "","User": "","AttachStdin": false,"AttachStdout": false,"AttachStderr": false,"ExposedPorts": {"80/tcp": {}},"Tty": false,"OpenStdin": false,"StdinOnce": false,"Env": ["PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin","NGINX_VERSION=1.25.2","NJS_VERSION=0.8.0","PKG_RELEASE=1~bookworm"],"Cmd": ["/bin/sh","-c","#(nop) ","CMD ["nginx""-g""daemon off;"]"],"Image": "sha256:d59ed5fe14c2a306f94488f41ddc8fb060312ee31997f5e077a4c4b29b19114e","Volumes": null,"WorkingDir": "","Entrypoint": ["/docker-entrypoint.sh"],"OnBuild": null,"Labels": {"maintainer": "NGINX Docker Maintainers <[email protected]>"},"StopSignal": "SIGQUIT"},"DockerVersion": "20.10.23","Author": "","Config": {"Hostname": "","Domainname": "","User": "","AttachStdin": false,"AttachStdout": false,"AttachStderr": false,"ExposedPorts": {"80/tcp": {}},"Tty": false,"OpenStdin": false,"StdinOnce": false,"Env": ["PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin","NGINX_VERSION=1.25.2","NJS_VERSION=0.8.0","PKG_RELEASE=1~bookworm"],"Cmd": ["nginx","-g","daemon off;"],"Image": "sha256:d59ed5fe14c2a306f94488f41ddc8fb060312ee31997f5e077a4c4b29b19114e","Volumes": null,"WorkingDir": "","Entrypoint": ["/docker-entrypoint.sh"],"OnBuild": null,"Labels": {"maintainer": "NGINX Docker Maintainers <[email protected]>"},"StopSignal": "SIGQUIT"},"Architecture": "amd64","Os": "linux","Size": 186639842,"VirtualSize": 186639842,"GraphDriver": {"Data": {"LowerDir": "/var/lib/docker/overlay2/d745cef5963eb1b97431486b30dd71b2605a380faba47690838107b3c4ba2630/diff:/var/lib/docker/overlay2/f5e313e6f689f2f9c1fa159b138722b9739a2de4d0a4c93449900387e7d8555e/diff:/var/lib/docker/overlay2/bfc2a784caec1d2b3a17b56679f7111bccb3f4b297e772af91ce1023f7cf5488/diff:/var/lib/docker/overlay2/00cdcd19f2471b5176a7f63b0bf41fc9a01322b25375675a2dc21decb9153ae1/diff:/var/lib/docker/overlay2/4145b85df78cb19147ad122bb6f2f1314b60d28ad1c459cfaec53587fa3c8131/diff:/var/lib/docker/overlay2/9024483c771500047fbde0bee5f5d356568fdec7c09d018a1bf0d7c01148b3c5/diff","MergedDir": "/var/lib/docker/overlay2/703253eda4146f25fd4689a106743bc91d0d435314cb210f8a6f35c988e60843/merged","UpperDir": "/var/lib/docker/overlay2/703253eda4146f25fd4689a106743bc91d0d435314cb210f8a6f35c988e60843/diff","WorkDir": "/var/lib/docker/overlay2/703253eda4146f25fd4689a106743bc91d0d435314cb210f8a6f35c988e60843/work"},"Name": "overlay2"},"RootFS": {"Type": "layers","Layers": ["sha256:511780f88f80081112aea1bfdca6c800e1983e401b338e20b2c6e97f384e4299","sha256:4713cb24eeff341d0c36343149beba247572a5ff65c2be5b5d9baafb345c7393","sha256:d0a62f56ef413f60049bc87e43e60032b2a2ab8d931e15b86ee0286c85ae91a2","sha256:8a7e12012e6f60450e6d2d777b2a2c2256d34a0ccd84d605f72cc5329a87c8b8","sha256:e161c3f476b5199ab13856c7e190ed12a6562b7be059c7026ae9f594e1abbcaf","sha256:6fb960878295b567d25900b590157b976d080340caeaa8bf8c46d38c01b4537d","sha256:563c64030925e9016a2329d3a2b7d47b0c90931baf5d2d0aa926c4c8d94ab894"]},"Metadata": {"LastTagTime": "0001-01-01T00:00:00Z"}}]
可以看到所有的层
理解
所有的 Docker 镜像都起始于一个基础镜像层，当进行修改或增加新的内容时，就会在当前镜像层之上，创建新的镜像层.
举一个简单的例子，假如基于 Ubuntu Linux 16.04 创建一个新的像，这就是新像的第一层如果在该像中添加 Pvthon包就会在基础镜像层之上创建第二个镜像层;如果继续添加一个安全补丁，就会创建第三个镜像层.该镜像当前已经包合3 个镜像层，如下图所示(这只是一个用于演示的很简单的例子)。
在添加额外的镜像层的同时，镜像始终保持是当前所有镜像的组合，理解这一点非常重要。下图中举了一个简单的例子，每个镜像层包含 3 个文件，而镜像包含了来自两个镜像层的 6 个文件。
Docker 通过存储引擎(新版本采用快照机制 的方式来实现像层堆栈，并保证多像层对外展示为统一的文件系统.
Linux 上可用的存诸引擎有 AUFS、Overlay2、 Device Mapper、 Btrfs 以及 ZFS顾名思义，每种存储引擎都基于 Linux 中对应的文件系统或者块设备技术，并且每种存储引擎都有其独有的性能特点。
Docker 在 Windows 上仅支持 windowsfilter -种存储引擎，该引擎基于 NTFS 文件系统之上实现了分层和 CoW[1]。
下图展示了与系统显示相同的三层镜像。所有镜像层堆叠并合并，对外提供统一的视图。
Docker镜像都是只读的，当容器启动时，一个新的可写层被加载到镜像的顶部, 这一层就是我们通常说的容器层，容器之下的都叫镜像层!
所有的操作都是基于容器层的。
Commit镜像
docker commit
docker commit 提交容器成为一个新的副本docker commit -m="提交的描述信息" -a="作者" 容器id 目标镜像名root@dev:~ # docker psCONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES9714036cd5ff portainer/portainer "/portainer" About an hour ago Up About an hour 8000/tcp, 9443/tcp, 0.0.0.0:8088->9000/tcp dreamy_mestorfd9acd4cf5818 nginx "/docker-entrypoint.…" 3 hours ago Up 3 hours 0.0.0.0:3340->80/tcp nginx0107ef5d12ab92 tensorflow/serving "/usr/bin/tf_serving…" 7 hours ago Up 7 hours 8500/tcp, 0.0.0.0:8501->8501/tcp clever_kirchroot@dev:~ # docker commit -m "测试commit" -a="myz" d9acd4cf5818 nginx:1.0sha256:d93c5436de5dc26f97aa9cedefa833bd6d519c3159fbd41f05424c33515bcf79root@dev:~ # docker imagesREPOSITORY TAG IMAGE ID CREATED SIZEnginx 1.0 d93c5436de5d 13 seconds ago 187MBtomcat latest 6adff26d17ba 13 hours ago 426MBnginx latest eea7b3dcba7e 21 hours ago 187MBtensorflow/serving latest 97e0a2470728 3 weeks ago 518MBtensorflow/serving <none> 07511a317c0b 3 months ago 510MBportainer/portainer latest 5f11582196a4 8 months ago 287MBcentos latest 5d0da3dc9764 23 months ago 231MBtensorflow/serving <none> e874bf5e4700 2 years ago 406MBhello-world latest d1165f221234 2 years ago 13.3kB
总结：
我们要打一个自己的镜像：1、下载要用的镜像，变成自己的容器，在容器中安装各种软件；2、从容器中commit ；
实战案例：
1、启动一个默认的tensorflow;
2、发现这个默认的tensorflow没有sklearn , 没有gensim, 没有pandas;
3、我们根据这个镜像启动一个容器；
4、我们往这个容器里头各种安装软件， 并且把我们自己的代码封装成库放到容器里；
5、将我们修改过的容器通过commit提交为一个镜像，比如4fun-recommend;
6、后续其他地方要用的时候，直接从4fun-recommend开启容器，并且所有环境都安装好了！！！
对于稳定的服务，极大的标准化和省略了安装配置的工作
容器数据卷
什么是容器数据卷
docker的理念
将应用和环境打包成一个镜像，那么数据怎么办，如果数据都在容器中，那么删除容器，数据就会丢失，需求：数据可以持久化
容器之间可以有一个数据共享的技术，Docker容器中产生的数据，同步到本地
这就是容器数据卷，目录的挂载，将我们容器内的目录，挂载到本地。
总结一句话：容器的持久化和同步操作，容器间也可以数据共享
使用数据卷
使用命令来挂载 -v
docker run -it -v 主机目录：容器内目录root@dev:/home/maoyaozong # docker run -it -v /home/maoyaozong:/home centos /bin/bash[root@9a627f0d4046 /]# lsbin dev etc home lib lib64 lost+found media mnt opt proc root run sbin srv sys tmp usr var[root@9a627f0d4046 /]# cd /home[root@9a627f0d4046 home]# lsrecommend-python[root@9a627f0d4046 home]# touch test.java[root@9a627f0d4046 home]# lsrecommend-python test.javaroot@dev:/home/maoyaozong # lltotal 4.0Kdrwxr-xr-x 9 root root 4.0K Jul 18 2022 recommend-pythonroot@dev:/home/maoyaozong # lsrecommend-python test.java
docker run -it -v /home/maoyaozong:/home centos /bin/bashSource： 主机内地址Destination : docker容器内地址root@dev:/home/maoyaozong # touch test1.java[root@9a627f0d4046 home]# lsrecommend-python test.java test1.java
就算是关掉了容器， 如果在本机继续修改目录， 重新启动容器，数据一样会同步
1、 CentOS已经没有运行了root@dev:/home/maoyaozong # docker psCONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES9714036cd5ff portainer/portainer "/portainer" 2 hours ago Up 2 hours 8000/tcp, 9443/tcp, 0.0.0.0:8088->9000/tcp dreamy_mestorfd9acd4cf5818 nginx "/docker-entrypoint.…" 5 hours ago Up 5 hours 0.0.0.0:3340->80/tcp nginx0107ef5d12ab92 tensorflow/serving "/usr/bin/tf_serving…" 8 hours ago Up 8 hours 8500/tcp, 0.0.0.0:8501->8501/tcp clever_kirch
2、在主机新加一个文件root@dev:/home/maoyaozong # touch test2.java3、重启容器并查看root@dev:/home/maoyaozong # docker start 9a627f0d40469a627f0d4046root@dev:/home/maoyaozong # lsrecommend-python test1.java test2.java test.java
我们的配置和数据，都可以挂载出来，后续在主机进行修改就不需要重启docker
具名和匿名挂载
匿名挂载
# 匿名挂载-v 容器内路径docker run -d -P --name nginx02 -v /etc/nginx nginx#-P 是随机映射端口root@dev:/home/maoyaozong # docker run -d -P --name nginx02 -v /etc/nginx nginx 125 ↵4862fd444c261f2b1bee7c5e7e6f964a0672ce295292ce291b66e170888e55e8root@dev:/home/maoyaozong # docker volume lsDRIVER VOLUME NAMElocal 2607062efba488ea3abafffdbf2fa3b5e04d7d7bdb8cf9c813261562aed1adeelocal e50d3a028a9f57a8512695e0cd71ad6b2eb2e6308f86359bca6b973e6b63d5f9
docker volume ls: 查看所有volume 本地的挂载
因为我们在 -v 的时候只写了容器内的路径，没有写容器外的路径
具名挂载
通过 -v 卷名：容器内路径
root@dev:/home/maoyaozong # docker run -d -P --name nginx03 -v have-name-nginx:/etc/nginx nginx84fe15fee4117ccce7f817eb7fc525fe2db845df84d19d90c7addffe2403e623root@dev:/home/maoyaozong # docker volume lsDRIVER VOLUME NAMElocal 2607062efba488ea3abafffdbf2fa3b5e04d7d7bdb8cf9c813261562aed1adeelocal e50d3a028a9f57a8512695e0cd71ad6b2eb2e6308f86359bca6b973e6b63d5f9local have-name-nginx
查看卷的挂载
root@dev:/home/maoyaozong # docker volume inspect have-name-nginx[{"CreatedAt": "2023-08-17T16:39:53+08:00","Driver": "local","Labels": null,"Mountpoint": "/var/lib/docker/volumes/have-name-nginx/_data","Name": "have-name-nginx","Options": {},"Scope": "local"}]
所有docker容器内的卷，没有指定目录的情况下都是在/var/lib/docker/volumes/xxxxx
root@dev:/home/maoyaozong # cd /var/lib/dockerroot@dev:/var/lib/docker # lsbuilder containerd containers image network overlay2 plugins runtimes swarm tmp trust volumesroot@dev:/var/lib/docker # cd volumesroot@dev:/var/lib/docker/volumes # lltotal 36Kdrwxr-xr-x 3 root root 4.0K Aug 17 14:12 2607062efba488ea3abafffdbf2fa3b5e04d7d7bdb8cf9c813261562aed1adeedrwxr-xr-x 3 root root 4.0K Aug 17 16:33 e50d3a028a9f57a8512695e0cd71ad6b2eb2e6308f86359bca6b973e6b63d5f9drwxr-xr-x 3 root root 4.0K Aug 17 16:39 have-name-nginx-rw------- 1 root root 32K Aug 17 16:39 metadata.dbroot@dev:/var/lib/docker/volumes # cd have-name-nginxroot@dev:/var/lib/docker/volumes/have-name-nginx # lltotal 4.0Kdrwxr-xr-x 3 root root 4.0K Aug 17 16:39 _dataroot@dev:/var/lib/docker/volumes/have-name-nginx # cd _dataroot@dev:/var/lib/docker/volumes/have-name-nginx/_data # lltotal 28Kdrwxr-xr-x 2 root root 4.0K Aug 17 16:39 conf.d-rw-r--r-- 1 root root 1007 Aug 16 01:03 fastcgi_params-rw-r--r-- 1 root root 5.3K Aug 16 01:03 mime.typeslrwxrwxrwx 1 root root 22 Aug 16 02:48 modules -> /usr/lib/nginx/modules-rw-r--r-- 1 root root 648 Aug 16 02:48 nginx.conf-rw-r--r-- 1 root root 636 Aug 16 01:03 scgi_params-rw-r--r-- 1 root root 664 Aug 16 01:03 uwsgi_params
我们通过具名挂载可以方便的找到我们的卷，大多数情况下最好使用具名挂载
如何确定是具名挂载还是匿名挂载还是指定路径挂载
-v 容器内路径 #匿名挂载
-v 卷名：容器内路径 # 具名挂载
-v /主机路径: 容器内路径 # 指定路径挂载
拓展:
ro, rw改变读写权限ro readonly #只读， 就说明这个路径只能通过宿主机来操作，容器内部是无法操作的rw readwrite #可读可写一旦这个设置了容器权限，容器对我们挂载出来的内容就有限定了docker run -d -P --name nginx03 -v have-name-nginx:/etc/nginx:ro nginxdocker run -d -P --name nginx03 -v have-name-nginx:/etc/nginx:rw nginx
--volumes-from: 容器数据同步
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首先启动一个父容器命名为test01
root@dev:/home/maoyaozong # docker run -it --name=test01 maoyaozong-centos2:1.0[root@47f5939e87a5 /]# #root@dev:/home/maoyaozong # docker psCONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES47f5939e87a5 maoyaozong-centos2:1.0 "/bin/sh -c /bin/bash" 6 seconds ago Up 5 seconds test01
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再启动一个子容器挂载到test01
root@dev:/home/maoyaozong # docker run -it --name=test02 --volumes-from test01 maoyaozong-centos2:1.0[root@1793455bbad3 /]# ls -ltotal 56lrwxrwxrwx 1 root root 7 Nov 3 2020 bin -> usr/bindrwxr-xr-x 5 root root 360 Aug 17 09:37 devdrwxr-xr-x 1 root root 4096 Aug 17 09:37 etcdrwxr-xr-x 2 root root 4096 Nov 3 2020 homelrwxrwxrwx 1 root root 7 Nov 3 2020 lib -> usr/liblrwxrwxrwx 1 root root 9 Nov 3 2020 lib64 -> usr/lib64drwx------ 2 root root 4096 Sep 15 2021 lost+founddrwxr-xr-x 2 root root 4096 Nov 3 2020 mediadrwxr-xr-x 2 root root 4096 Nov 3 2020 mntdrwxr-xr-x 2 root root 4096 Nov 3 2020 optdr-xr-xr-x 418 root root 0 Aug 17 09:37 procdr-xr-x--- 2 root root 4096 Sep 15 2021 rootdrwxr-xr-x 11 root root 4096 Sep 15 2021 runlrwxrwxrwx 1 root root 8 Nov 3 2020 sbin -> usr/sbindrwxr-xr-x 2 root root 4096 Nov 3 2020 srvdr-xr-xr-x 13 root root 0 Aug 17 09:21 sysdrwxrwxrwt 7 root root 4096 Sep 15 2021 tmpdrwxr-xr-x 12 root root 4096 Sep 15 2021 usrdrwxr-xr-x 20 root root 4096 Sep 15 2021 vardrwxr-xr-x 2 root root 4096 Aug 17 09:36 volume01drwxr-xr-x 2 root root 4096 Aug 17 09:36 volume02
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进入test01容器，在volume01中新建文件
root@dev:/home/maoyaozong/docker-test-volume # docker attach test01drwxr-xr-x 2 root root 4096 Aug 17 09:36 volume02[root@47f5939e87a5 /]# cd volume01[root@47f5939e87a5 volume01]# ls[root@47f5939e87a5 volume01]# touch xxxxxxxxxxxxx.java
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在test02容器里，查看volume01中是否有新文件
[][]xxxxxxxxxxxxx.java
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test01相当于java中的父类， test02通过volumes-from继承了test01 , 也称为数据卷容器；
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如果在新建一个test03, 挂载了test01 , 那么三个容器之间的数据都是共享的；
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修改test01, test02, test03 任何一个数据都是实现同步；
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即使删除test01, 保存在test02, test03的数据不会丢失，只要有一个容器还在就行；
结论：
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容器之间配置信息的传递，数据卷容器的生命周期一直持续到没有容器使用位置。
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但是一旦持久化到了本地，这个时候，本地的数据是不会删除的。
原文始发于微信公众号（风物长宜 AI）：Docker 容器技术
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