e611a189cb
If a logdriver doesn't register a callback function to validate log options, it won't be usable. Fix the journald driver by adding a dummy validator. Teach the client and the daemon's "logs" logic that the server can also supply "logs" data via the "journald" driver. Update documentation and tests that depend on error messages. Add support for reading log data from the systemd journal to the journald log driver. The internal logic uses a goroutine to scan the journal for matching entries after any specified cutoff time, formats the messages from those entries as JSONLog messages, and stuffs the results down a pipe whose reading end we hand back to the caller. If we are missing any of the 'linux', 'cgo', or 'journald' build tags, however, we don't implement a reader, so the 'logs' endpoint will still return an error. Make the necessary changes to the build setup to ensure that support for reading container logs from the systemd journal is built. Rename the Jmap member of the journald logdriver's struct to "vars" to make it non-public, and to make it easier to tell that it's just there to hold additional variable values that we want journald to record along with log data that we're sending to it. In the client, don't assume that we know which logdrivers the server implements, and remove the check that looks at the server. It's redundant because the server already knows, and the check also makes using older clients with newer servers (which may have new logdrivers in them) unnecessarily hard. When we try to "logs" and have to report that the container's logdriver doesn't support reading, send the error message through the might-be-a-multiplexer so that clients which are expecting multiplexed data will be able to properly display the error, instead of tripping over the data and printing a less helpful "Unrecognized input header" error. Signed-off-by: Nalin Dahyabhai <nalin@redhat.com> (github: nalind) |
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Docker: the container engine 
Docker is an open source project to pack, ship and run any application as a lightweight container.
Docker containers are both hardware-agnostic and platform-agnostic. This means they can run anywhere, from your laptop to the largest cloud compute instance and everything in between - and they don't require you to use a particular language, framework or packaging system. That makes them great building blocks for deploying and scaling web apps, databases, and backend services without depending on a particular stack or provider.
Docker began as an open-source implementation of the deployment engine which powers dotCloud, a popular Platform-as-a-Service. It benefits directly from the experience accumulated over several years of large-scale operation and support of hundreds of thousands of applications and databases.
Security Disclosure
Security is very important to us. If you have any issue regarding security, please disclose the information responsibly by sending an email to security@docker.com and not by creating a github issue.
Better than VMs
A common method for distributing applications and sandboxing their execution is to use virtual machines, or VMs. Typical VM formats are VMware's vmdk, Oracle VirtualBox's vdi, and Amazon EC2's ami. In theory these formats should allow every developer to automatically package their application into a "machine" for easy distribution and deployment. In practice, that almost never happens, for a few reasons:
- Size: VMs are very large which makes them impractical to store and transfer.
- Performance: running VMs consumes significant CPU and memory, which makes them impractical in many scenarios, for example local development of multi-tier applications, and large-scale deployment of cpu and memory-intensive applications on large numbers of machines.
- Portability: competing VM environments don't play well with each other. Although conversion tools do exist, they are limited and add even more overhead.
- Hardware-centric: VMs were designed with machine operators in mind, not software developers. As a result, they offer very limited tooling for what developers need most: building, testing and running their software. For example, VMs offer no facilities for application versioning, monitoring, configuration, logging or service discovery.
By contrast, Docker relies on a different sandboxing method known as containerization. Unlike traditional virtualization, containerization takes place at the kernel level. Most modern operating system kernels now support the primitives necessary for containerization, including Linux with openvz, vserver and more recently lxc, Solaris with zones, and FreeBSD with Jails.
Docker builds on top of these low-level primitives to offer developers a portable format and runtime environment that solves all four problems. Docker containers are small (and their transfer can be optimized with layers), they have basically zero memory and cpu overhead, they are completely portable, and are designed from the ground up with an application-centric design.
Perhaps best of all, because Docker operates at the OS level, it can still be run inside a VM!
Plays well with others
Docker does not require you to buy into a particular programming language, framework, packaging system, or configuration language.
Is your application a Unix process? Does it use files, tcp connections, environment variables, standard Unix streams and command-line arguments as inputs and outputs? Then Docker can run it.
Can your application's build be expressed as a sequence of such commands? Then Docker can build it.
Escape dependency hell
A common problem for developers is the difficulty of managing all their application's dependencies in a simple and automated way.
This is usually difficult for several reasons:
-
Cross-platform dependencies. Modern applications often depend on a combination of system libraries and binaries, language-specific packages, framework-specific modules, internal components developed for another project, etc. These dependencies live in different "worlds" and require different tools - these tools typically don't work well with each other, requiring awkward custom integrations.
-
Conflicting dependencies. Different applications may depend on different versions of the same dependency. Packaging tools handle these situations with various degrees of ease - but they all handle them in different and incompatible ways, which again forces the developer to do extra work.
-
Custom dependencies. A developer may need to prepare a custom version of their application's dependency. Some packaging systems can handle custom versions of a dependency, others can't - and all of them handle it differently.
Docker solves the problem of dependency hell by giving the developer a simple way to express all their application's dependencies in one place, while streamlining the process of assembling them. If this makes you think of XKCD 927, don't worry. Docker doesn't replace your favorite packaging systems. It simply orchestrates their use in a simple and repeatable way. How does it do that? With layers.
Docker defines a build as running a sequence of Unix commands, one after the other, in the same container. Build commands modify the contents of the container (usually by installing new files on the filesystem), the next command modifies it some more, etc. Since each build command inherits the result of the previous commands, the order in which the commands are executed expresses dependencies.
Here's a typical Docker build process:
FROM ubuntu:12.04
RUN apt-get update && apt-get install -y python python-pip curl
RUN curl -sSL https://github.com/shykes/helloflask/archive/master.tar.gz | tar -xzv
RUN cd helloflask-master && pip install -r requirements.txt
Note that Docker doesn't care how dependencies are built - as long as they can be built by running a Unix command in a container.
Getting started
Docker can be installed on your local machine as well as servers - both bare metal and virtualized. It is available as a binary on most modern Linux systems, or as a VM on Windows, Mac and other systems.
We also offer an interactive tutorial for quickly learning the basics of using Docker.
For up-to-date install instructions, see the Docs.
Usage examples
Docker can be used to run short-lived commands, long-running daemons (app servers, databases, etc.), interactive shell sessions, etc.
You can find a list of real-world examples in the documentation.
Under the hood
Under the hood, Docker is built on the following components:
- The cgroups and namespaces capabilities of the Linux kernel
- The Go programming language
- The Docker Image Specification
- The Libcontainer Specification
Contributing to Docker 
| Master (Linux) | Experimental (linux) | Windows | FreeBSD |
|---|---|---|---|
Want to hack on Docker? Awesome! We have instructions to help you get started contributing code or documentation.
These instructions are probably not perfect, please let us know if anything feels wrong or incomplete. Better yet, submit a PR and improve them yourself.
Getting the development builds
Want to run Docker from a master build? You can download master builds at master.dockerproject.org. They are updated with each commit merged into the master branch.
Don't know how to use that super cool new feature in the master build? Check out the master docs at docs.master.dockerproject.org.
How the project is run
Docker is a very, very active project. If you want to learn more about how it is run, or want to get more involved, the best place to start is the project directory.
We are always open to suggestions on process improvements, and are always looking for more maintainers.
Talking to other Docker users and contributors
| Internet Relay Chat (IRC) |
IRC a direct line to our most knowledgeable Docker users; we have
both the |
| Google Groups | There are two groups. Docker-user is for people using Docker containers. The docker-dev group is for contributors and other people contributing to the Docker project. |
| You can follow Docker's Twitter feed to get updates on our products. You can also tweet us questions or just share blogs or stories. | |
| Stack Overflow | Stack Overflow has over 7000 Docker questions listed. We regularly monitor Docker questions and so do many other knowledgeable Docker users. |
Legal
Brought to you courtesy of our legal counsel. For more context, please see the NOTICE document in this repo.
Use and transfer of Docker may be subject to certain restrictions by the United States and other governments.
It is your responsibility to ensure that your use and/or transfer does not violate applicable laws.
For more information, please see https://www.bis.doc.gov
Licensing
Docker is licensed under the Apache License, Version 2.0. See LICENSE for the full license text.
Other Docker Related Projects
There are a number of projects under development that are based on Docker's core technology. These projects expand the tooling built around the Docker platform to broaden its application and utility.
- Docker Registry: Registry server for Docker (hosting/delivery of repositories and images)
- Docker Machine: Machine management for a container-centric world
- Docker Swarm: A Docker-native clustering system
- Docker Compose (formerly Fig): Define and run multi-container apps
- Kitematic: The easiest way to use Docker on Mac and Windows
If you know of another project underway that should be listed here, please help us keep this list up-to-date by submitting a PR.
Awesome-Docker
You can find more projects, tools and articles related to Docker on the awesome-docker list. Add your project there.