Models & Data Structures

This outline provides insights into the models and data structures used within the Docker SDK, helping developers understand the representation of Docker objects within the library.

Why Use Models?

Models offer a structured approach to representing Docker objects, enhancing readability and simplifying interactions with the Docker API. By encapsulating data and providing methods for accessing and manipulating it, they streamline the process of working with Docker components.

Key Models and Their Purpose

• Container: Represents a Docker container. It encapsulates attributes like image name, command, ports, and environment variables. Source: docker/models/containers.py

  # Example of using Container model
            from docker.models.containers import Container
            
            container = Container.create(image='ubuntu:latest', command=['echo', 'Hello, Docker!'])
            container.start()
            container.wait()
            container.logs() 
            

• Image: Represents a Docker image. It stores information about the image, including its tag, repository, and size. Source: docker/models/images.py

  # Example of using Image model
            from docker.models.images import Image
            
            image = Image.build(path='.', tag='my-app:latest')
            image.push(repository='my-docker-hub-repo', tag='latest')
            

• Network: Represents a Docker network. It encapsulates network configuration details, including name, driver, and connectivity settings. Source: docker/models/networks.py

  # Example of using Network model
            from docker.models.networks import Network
            
            network = Network.create(name='my-network', driver='bridge')
            container = Container.create(image='ubuntu:latest', network=network.id)
            container.start()
            

• Volume: Represents a Docker volume. It holds data that persists even after containers using it are removed. Source: docker/models/volumes.py

  # Example of using Volume model
            from docker.models.volumes import Volume
            
            volume = Volume.create(name='my-volume')
            container = Container.create(image='ubuntu:latest', volumes={'my-volume': {'bind': '/data'}})
            container.start()
            

Benefits of Using Models

• Abstraction: Models provide a higher-level abstraction, shielding users from the intricacies of the underlying Docker API. This abstraction simplifies interactions, enhancing code readability.
• Data Consistency: Models ensure consistent data representation across the SDK, making data handling more reliable.
• Code Reusability: Models promote code reusability by encapsulating common Docker operations, reducing redundancy and improving maintainability.

Accessing Model Data

Model data is accessible through various methods:

• Attributes: Accessing data through attributes, such as container.image or image.tag.
• Methods: Using methods to retrieve or manipulate data. For example, container.logs() or image.history().

Example:

from docker.models.containers import Container
          
          # Create a container
          container = Container.create(image='ubuntu:latest', command=['echo', 'Hello, Docker!'])
          
          # Access container attributes
          print(container.image)  # Output: ubuntu:latest
          print(container.command) # Output: ['echo', 'Hello, Docker!']
          
          # Start and wait for the container
          container.start()
          container.wait()
          
          # Get container logs
          logs = container.logs()
          print(logs.decode()) 
          

Note: The docker-py project is an active one, and models may evolve over time. Refer to the official documentation for up-to-date information on available models and their attributes.