Docker Engine is 100% free. It is open source, so you can use it without paying.
We are using the Apache License Version 2.0, see it here: https://github.com/docker/docker/blob/master/LICENSE
Docker Engine currently runs only on Linux, but you can use VirtualBox to run Engine in a virtual machine on your box, and get the best of both worlds. Check out the Mac OS X and Microsoft Windows installation guides. The small Linux distribution boot2docker can be set up using the Docker Machine tool to be run inside virtual machines on these two operating systems.
Note: if you are using a remote Docker Engine daemon on a VM through Docker Machine, then do not type the
dockercommands shown in the documentation's examples.
They are complementary. VMs are best used to allocate chunks of hardware resources. Containers operate at the process level, which makes them very lightweight and perfect as a unit of software delivery.
Docker technology is not a replacement for LXC. “LXC” refers to capabilities of the Linux kernel (specifically namespaces and control groups) which allow sandboxing processes from one another, and controlling their resource allocations. On top of this low-level foundation of kernel features, Docker offers a high-level tool with several powerful functionalities:
Portable deployment across machines. Docker defines a format for bundling an application and all its dependencies into a single object which can be transferred to any Docker-enabled machine, and executed there with the guarantee that the execution environment exposed to the application will be the same. LXC implements process sandboxing, which is an important pre-requisite for portable deployment, but that alone is not enough for portable deployment. If you sent me a copy of your application installed in a custom LXC configuration, it would almost certainly not run on my machine the way it does on yours, because it is tied to your machine's specific configuration: networking, storage, logging, distro, etc. Docker defines an abstraction for these machine-specific settings, so that the exact same Docker container can run - unchanged - on many different machines, with many different configurations.
Application-centric. Docker is optimized for the deployment of applications, as opposed to machines. This is reflected in its API, user interface, design philosophy and documentation. By contrast, the
lxc helper scripts focus on containers as lightweight machines - basically servers that boot faster and need less RAM. We think there's more to containers than just that.
Automatic build. Docker includes a tool for developers to automatically assemble a container from their source code, with full control over application dependencies, build tools, packaging etc. They are free to use
salt, Debian packages, RPMs, source tarballs, or any combination of the above, regardless of the configuration of the machines.
Versioning. Docker includes git-like capabilities for tracking successive versions of a container, inspecting the diff between versions, committing new versions, rolling back etc. The history also includes how a container was assembled and by whom, so you get full traceability from the production server all the way back to the upstream developer. Docker also implements incremental uploads and downloads, similar to
git pull, so new versions of a container can be transferred by only sending diffs.
Component re-use. Any container can be used as a “base image” to create more specialized components. This can be done manually or as part of an automated build. For example you can prepare the ideal Python environment, and use it as a base for 10 different applications. Your ideal PostgreSQL setup can be re-used for all your future projects. And so on.
Sharing. Docker has access to a public registry on Docker Hub where thousands of people have uploaded useful images: anything from Redis, CouchDB, PostgreSQL to IRC bouncers to Rails app servers to Hadoop to base images for various Linux distros. The registry also includes an official “standard library” of useful containers maintained by the Docker team. The registry itself is open-source, so anyone can deploy their own registry to store and transfer private containers, for internal server deployments for example.
Tool ecosystem. Docker defines an API for automating and customizing the creation and deployment of containers. There are a huge number of tools integrating with Docker to extend its capabilities. PaaS-like deployment (Dokku, Deis, Flynn), multi-node orchestration (Maestro, Salt, Mesos, Openstack Nova), management dashboards (docker-ui, Openstack Horizon, Shipyard), configuration management (Chef, Puppet), continuous integration (Jenkins, Strider, Travis), etc. Docker is rapidly establishing itself as the standard for container-based tooling.
There's a great StackOverflow answer showing the differences.
Not at all! Any data that your application writes to disk gets preserved in its container until you explicitly delete the container. The file system for the container persists even after the container halts.
Some of the largest server farms in the world today are based on containers. Large web deployments like Google and Twitter, and platform providers such as Heroku and dotCloud all run on container technology, at a scale of hundreds of thousands or even millions of containers running in parallel.
Currently the recommended way to connect containers is via the Docker network feature. You can see details of how to work with Docker networks here.
Also useful for more flexible service portability is the Ambassador linking pattern.
Any capable process supervisor such as http://supervisord.org/, runit, s6, or daemontools can do the trick. Docker will start up the process management daemon which will then fork to run additional processes. As long as the processor manager daemon continues to run, the container will continue to as well. You can see a more substantial example that uses supervisord here.
Please read our blog post on the introduction of the DCO.
This is a summary of a discussion on the docker-dev mailing list.
Virtually all programs depend on third-party libraries. Most frequently, they will use dynamic linking and some kind of package dependency, so that when multiple programs need the same library, it is installed only once.
Some programs, however, will bundle their third-party libraries, because they rely on very specific versions of those libraries. For instance, Node.js bundles OpenSSL; MongoDB bundles V8 and Boost (among others).
When creating a Docker image, is it better to use the bundled libraries, or should you build those programs so that they use the default system libraries instead?
The key point about system libraries is not about saving disk or memory space. It is about security. All major distributions handle security seriously, by having dedicated security teams, following up closely with published vulnerabilities, and disclosing advisories themselves. (Look at the Debian Security Information for an example of those procedures.) Upstream developers, however, do not always implement similar practices.
Before setting up a Docker image to compile a program from source, if you want to use bundled libraries, you should check if the upstream authors provide a convenient way to announce security vulnerabilities, and if they update their bundled libraries in a timely manner. If they don't, you are exposing yourself (and the users of your image) to security vulnerabilities.
Likewise, before using packages built by others, you should check if the channels providing those packages implement similar security best practices. Downloading and installing an “all-in-one” .deb or .rpm sounds great at first, except if you have no way to figure out that it contains a copy of the OpenSSL library vulnerable to the Heartbleed bug.
When building Docker images on Debian and Ubuntu you may have seen errors like:
unable to initialize frontend: Dialog
These errors don't stop the image from being built but inform you that the installation process tried to open a dialog box, but was unable to. Generally, these errors are safe to ignore.
Some people circumvent these errors by changing the
DEBIAN_FRONTEND environment variable inside the Dockerfile using:
This prevents the installer from opening dialog boxes during installation which stops the errors.
While this may sound like a good idea, it may have side effects. The
DEBIAN_FRONTEND environment variable will be inherited by all images and containers built from your image, effectively changing their behavior. People using those images will run into problems when installing software interactively, because installers will not show any dialog boxes.
Because of this, and because setting
noninteractive is mainly a ‘cosmetic’ change, we discourage changing it.
If you really need to change its setting, make sure to change it back to its default value afterwards.
Typically, this message is returned if the service is already bound to your localhost. As a result, requests coming to the container from outside are dropped. To correct this problem, change the service‘s configuration on your localhost so that the service accepts requests from all IPs. If you aren’t sure how to do this, check the documentation for your OS.
Cannot connect to the Docker daemon. Is the docker daemon running on this host?when using docker-machine?
This error points out that the docker client cannot connect to the virtual machine. This means that either the virtual machine that works underneath
docker-machine is not running or that the client doesn't correctly point at it.
To verify that the docker machine is running you can use the
docker-machine ls command and start it with
docker-machine start if needed.
$ docker-machine ls NAME ACTIVE DRIVER STATE URL SWARM DOCKER ERRORS default - virtualbox Stopped Unknown $ docker-machine start default
You have to tell Docker to talk to that machine. You can do this with the
docker-machine env command. For example,
$ eval "$(docker-machine env default)" $ docker ps
You can find more answers on:
Looking for something else to read? Checkout the User Guide.