docker-map 1.1.1

Managing Docker images, containers, and their dependencies in Python.

Docker-Map

Managing Docker images, containers, and their dependencies in Python.

Project: https://github.com/merll/docker-map

Docs: https://docker-map.readthedocs.io/en/latest/

Overview

This package provides tools for building Docker images, create containers, connect dependent resources, and run them in development as well as production environments.

The library builds on functionality of the Docker Remote API client for Python, docker-py. Its main target is to reduce the repetitive and error-prone code that is required for creating and connecting containers in a non-trivial stack. It can be used standalone for custom orchestration or for enhancing available deployment / remote execution utilities (see Docker-Fabric, Salt Container-Map).

Containers and their dependencies are configured object-based, through Python dictionaries, or YAML files.

Building images

Writing Dockerfiles is not hard. However, it only allows for using variable context to a limited extent. For example, you may want to re-define directory paths in your project, without having to adjust it in multiple places; or you keep frequently reoccurring tasks (e.g. creating system user accounts) in your Dockerfile, and would like to use templates rather than copy & paste.

Dockerfiles

A DockerFile object generates a Dockerfile, that can either be saved locally or sent off to Docker through the remote API. Supports common commands such as addfile (ADD) or run, but also formats CMD and ENTRYPOINT appropriately for running a shell or exec command.

Docker file context

DockerContext generates a Docker context tarball, that can be sent to the remote API. Its main purpose is to add files from DockerFile automatically, so that the Dockerfile and the context tarball are consistent.

Creating, connecting, and running containers

This package reduces repetitions of names and paths in API commands, by introducing the following main features:

• Automatically create, configure, and assign shared volumes.

• Automatically update containers if their shared volumes are inconsistent, their image, or their configuration has been updated.

• Use alias names instead of paths to bind host volumes to container shares.

• Automatically create and start containers when their dependent containers are started.

Container configuration

ContainerConfiguration objects keep the elements of a configured container. Their main elements are:

• image: Docker image to base the container on (default is identical to container name).

• clients: Optional list of clients to run the identical container configuration on.

• instances: Can generate multiple instances of a container with varying host mappings; by default there is one main instance of each container.

• shares: Volumes that are simply shared by the container, only for the purpose of keeping data separate from the container instance, or for linking the entire container to another.

• binds: Host volume mappings. Uses alias names instead of directory paths.

• uses: Can be names of other containers, or volumes shared by another volume through attaches. Has the same effect as the volumes_from argument in the API, but using alias names and automatically resolving these to paths.

• links: For container linking. Container names are translated to instance name on the map.

• attaches: Generates a separate container for the purpose of sharing data with another one, assigns file system permissions as set in permissions and user. This makes configuration of sockets very easy.

• exposes: Configures port bindings for linked containers and on host interfaces.

• exec_commands: Launches commands on containers after they have been created and started.

• create_options and host_config provide the possibility to add further keyword arguments such as command or entrypoint, which are passed through to the docker-py client.

Container maps

ContainerMap objects contain three sets of elements:

1. Container names, each associated with a ContainerConfiguration.

2. Volumes, mapping shared directory paths to alias names.

3. Host shares, mapping host directory paths to alias names.

Clients, as defined in a ContainerConfiguration, can also be set globally on map level.

ContainerConfiguration instances and their elements can be created and used in a dictionary-like or attribute syntax, e.g. container_map.containers.container_name.uses or container_map.containers['container_name']['uses']. Volume aliases are stored in container_map.volumes and host binds in container_map.host; they support the same syntax variations as containers.

Client configuration

ClientConfiguration objects allow for a host-specific management of parameters, such as service URL and timeout. For example, the interfaces property translates the exposes setting for a configuration on each host into a port binding argument with the local address.

Combining the elements

MappingDockerClient applies one or multiple ContainerMap instances to one or multiple Docker clients. A container on the map can easily be created with all its dependencies by running client.create('container_name').

Running the container can be as easy as client.start('container_name') or can be enhanced with custom parameters such as client.start('container_name', expose={80: 80}).

If all configuration is stored on the map, creation and start are combined in client.startup('container_name').