Docker：容器与本地安装 [英] Docker: containers vs local installs

问题描述

在Docker上周末播放了Docker，并且看到从irssi，mutt，浏览器等所有的小图像，我想知道本地安装的软件包是否正在为数十个容器做出改变？ / p>

我可以看到保持基本系统非常干净的好处，并且所有这些容器都是独立的，并且可以轻松地重新定位到不同的桌面，甚至Windows。每个运行一个像阿尔卑斯山这样的小型发行版。 irssi等....

这是事情正在走向还是我在这里丢失船的方式？

解决方案

Jess Frazelle 不会不同意你的意见。

在她身上博客桌面上的Docker容器，她正在集装箱化所有内容。

喜欢 Chrome本身：

  $ docker run -it \ 
 --net host \\ \\＃也可以YOLO 
 --cpuset-cpus 0 \＃控制cpu 
 --memory 512mb \＃max内存，它可以使用
 -v /tmp/.X11- unix：/tmp/.X11-unix \＃mount X11插座
 -e DISPLAY = unix $ DISPLAY \＃通过显示
 -v $ HOME / Downloads：/ root / Downloads \ ＃可选，但是
 -v $ HOME / .config / google-chrome /：/ data \＃如果要保存状态
 --device / dev / snd \＃所以我们有sound 
 --name chrome \ 
 jess / chrome 
  

但是Docker容器并不仅限于这种用途，而且主要是为每个容器提供一个服务来表示稳定的，明确且可重现的执行环境的方式，您可以从开发工作站直到生产服务器。

After playing around with Docker for the first time over the week-end and seeing tiny images for everything from irssi, mutt, browsers, etc, I was wondering if local installs of packages are making way for dozens of containers instead?

I can see the benefit in keeping the base system very clean and having all these containers that are all self-contained and could be easily relocated to different desktops, even Windows. Each running a tiny distro like Alpine, with the app e.g. irssi, etc....

Is this the way things are moving towards or am I missing the boat here?

解决方案

Jess Frazelle would not disagree with you.
In her blog post "Docker Containers on the Desktop", she is containerizing everything. Everything.

Like Chrome itself:

$ docker run -it \
    --net host \ # may as well YOLO
    --cpuset-cpus 0 \ # control the cpu
    --memory 512mb \ # max memory it can use
    -v /tmp/.X11-unix:/tmp/.X11-unix \ # mount the X11 socket
    -e DISPLAY=unix$DISPLAY \ # pass the display
    -v $HOME/Downloads:/root/Downloads \ # optional, but nice
    -v $HOME/.config/google-chrome/:/data \ # if you want to save state
    --device /dev/snd \ # so we have sound
    --name chrome \
    jess/chrome

But Docker containers are not limited to that usage, and are mainly a way to represent a stable well-defined and reproducible execution environment, for one service per container, that you can use from a development workstation up to a production server.

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