Docker

1. Docker revolutionizes application deployment and scaling

Docker sits right in the middle of some of the most enabling technologies of the last decade.

Simplified deployment. Docker allows developers to package applications with all their dependencies into standardized units called containers. This approach dramatically simplifies the deployment process, ensuring consistency across different environments.

Improved scalability. By abstracting away the underlying infrastructure, Docker enables applications to be easily scaled horizontally. Containers can be quickly spun up or down based on demand, allowing for more efficient resource utilization.

DevOps enablement. Docker bridges the gap between development and operations teams by providing a common language and toolset. This facilitates better collaboration and smoother workflows throughout the application lifecycle.

2. Containers offer lightweight, portable, and efficient virtualization

Containers are a fundamentally different approach where all containers share a single kernel and isolation is implemented entirely within that single kernel.

Resource efficiency. Containers share the host system's kernel, making them significantly lighter than traditional virtual machines. This allows for higher density of applications on a single host and faster startup times.

Portability. Docker containers encapsulate the application and its dependencies, ensuring consistent behavior across different environments. This "build once, run anywhere" approach simplifies development and deployment workflows.

Isolation. While not providing the same level of isolation as virtual machines, containers offer sufficient separation for most use cases. They utilize Linux kernel features like namespaces and cgroups to create isolated environments for applications.

3. Docker architecture: client, server, and registry

Docker consists of at least two parts: the client and the server/daemon (see Figure 2-3). Optionally there is a third component called the registry, which stores Docker images and metadata about those images.

Client-server model. Docker uses a client-server architecture where the Docker client communicates with the Docker daemon, which handles building, running, and distributing containers.

Docker registry. The registry is a centralized repository for storing and distributing Docker images. Docker Hub is the public registry maintained by Docker, but organizations can also set up private registries.

Components interaction:

• Docker client: Sends commands to the Docker daemon
• Docker daemon: Manages Docker objects (images, containers, networks, volumes)
• Docker registry: Stores Docker images

4. Building and managing Docker images and containers

Containers are normally designed to be disposable, you may still find that standard testing is not always sufficient to avoid all problems and that you will want some tools for debugging running containers.

Image creation. Docker images are built using Dockerfiles, which contain a series of instructions for creating the image. Each instruction creates a new layer, allowing for efficient storage and transfer of images.

Container lifecycle:

• Create: docker create
• Start: docker start
• Run: docker run (combines create and start)
• Stop: docker stop
• Remove: docker rm

Debugging tools:

• docker logs: View container logs
• docker exec: Run commands inside a running container
• docker inspect: Get detailed information about Docker objects

5. Networking and storage in Docker environments

Docker allocates the private subnet from an unused RFC 1918 private subnet block. It detects which network blocks are unused on startup and allocates one to the virtual network.

Networking models:

• Bridge: Default network driver, creating a private network for containers
• Host: Removes network isolation, using the host's network directly
• Overlay: Enables communication between containers across multiple Docker hosts
• Macvlan: Assigns a MAC address to a container, making it appear as a physical device on the network

Storage options:

• Volumes: Preferred mechanism for persistent data, managed by Docker
• Bind mounts: Map a host file or directory to a container
• tmpfs mounts: Store data temporarily in the host's memory

6. Debugging and monitoring Docker containers

There are times when you really just want to stop our container as described above. But there are a number of times when we just don't want our container to do anything for a while.

Debugging techniques:

• docker logs: View container output
• docker exec: Run commands inside a running container
• docker inspect: Get detailed information about containers
• docker stats: Monitor container resource usage in real-time

Monitoring tools:

• cAdvisor: Provides resource usage and performance data
• Prometheus: Collect and store metrics from containers
• Grafana: Visualize container metrics and create dashboards

7. Scaling Docker with orchestration tools

Probably the first publicly available tool in this arena is Fleet from CoreOS, which works with systemd on the hosts to act as a distributed init system.

Orchestration platforms:

• Docker Swarm: Native clustering for Docker
• Kubernetes: Open-source container orchestration platform
• Apache Mesos: Distributed systems kernel that can run Docker containers

Key features:

• Service discovery
• Load balancing
• Scaling
• Rolling updates
• Self-healing

8. Security considerations for Docker deployments

Because it's a daemon that runs with privilege, and because it has direct control of your applications, it's probably not a good idea to expose Docker directly on the Internet.

Security best practices:

• Run containers as non-root users
• Use minimal base images to reduce attack surface
• Implement network segmentation
• Regularly update and patch Docker and container images
• Use Docker Content Trust for image signing and verification

Security tools:

• AppArmor/SELinux: Mandatory access control systems
• Docker Bench Security: Automated security assessment tool
• Clair: Open-source vulnerability scanner for containers

9. Designing a production-ready Docker platform

If, instead of simply deploying Docker into your environment, you take the time to build a well-designed container platform on top of Docker, you can enjoy the many benefits of a Docker-based workflow while protecting yourself from some of the sharper exposed edges that typically exist in such a high-velocity project.

Key considerations:

• High availability and fault tolerance
• Scalability and performance
• Monitoring and logging
• Backup and disaster recovery
• Continuous integration and deployment (CI/CD)

Best practices:

• Use orchestration tools for managing large-scale deployments
• Implement proper logging and monitoring solutions
• Develop a robust CI/CD pipeline for container builds and deployments
• Regularly test and update your Docker infrastructure

10. The Twelve-Factor App methodology for containerized applications

Although not required, applications built with these 12 steps in mind are ideal candidates for the Docker workflow.

Key principles:

1. Codebase: One codebase tracked in revision control, many deploys
2. Dependencies: Explicitly declare and isolate dependencies
3. Config: Store config in the environment
4. Backing services: Treat backing services as attached resources
5. Build, release, run: Strictly separate build and run stages
6. Processes: Execute the app as one or more stateless processes
7. Port binding: Export services via port binding
8. Concurrency: Scale out via the process model
9. Disposability: Maximize robustness with fast startup and graceful shutdown
10. Dev/prod parity: Keep development, staging, and production as similar as possible
11. Logs: Treat logs as event streams
12. Admin processes: Run admin/management tasks as one-off processes

Benefits for Docker applications:

• Improved scalability and maintainability
• Easier deployment and operations
• Better alignment with cloud-native architectures

Last updated: September 8, 2024