A Comprehensive Introduction to Docker, Virtual Machines, and Containers

In recent years, Docker has taken the software development world by storm. But what exactly is Docker, and how does it relate to virtual machines and containers? In this article, we‘ll take a deep dive into these technologies, explore their pros and cons, and see how Docker can streamline your development and deployment workflows.

What is Virtualization?

At its core, Docker leverages a technology called virtualization. Virtualization allows running multiple isolated environments on a single physical machine. There are two main approaches to virtualization:

Hardware Virtualization: With hardware virtualization, a piece of software called a hypervisor creates and runs virtual machines (VMs). Each VM has its own operating system (OS) and behaves like an independent computer, even though it is running on the same physical hardware. Examples of hypervisors include VirtualBox, VMware, and Hyper-V.

OS-Level Virtualization: In contrast, OS-level virtualization, also known as containerization, does not simulate a full hardware stack. Instead, the OS kernel itself provides isolated user-space instances called containers. All containers share the same OS kernel, but each has its own filesystem, processes, memory, devices, and network stack. This makes containers much more lightweight and faster to start up compared to VMs.

Virtual Machines: Pros and Cons

VMs have been a mainstay of IT infrastructure for decades. They allow running different operating systems and their associated workloads on the same server, increasing utilization and reducing costs. VMs also provide strong isolation between applications, as each has its own virtual hardware.

However, VMs have some drawbacks:

• Each VM requires its own OS, consuming significant storage space and memory
• Booting up a new VM is slow, often taking minutes
• VM disk images are large, making them cumbersome to store and transfer
• The hypervisor adds performance overhead compared to running on bare metal

As a result, while VMs work well for many scenarios, they are not ideal for fast-moving, distributed applications that need to scale quickly and make efficient use of resources. This is where containers shine.

The Rise of Containers

Containers aim to provide the isolation and portability of VMs, but in a more lightweight and agile manner. By sharing the same OS kernel and libraries, containers have a much smaller footprint than VMs. This allows spinning up new containers in milliseconds and packing many more containers onto a single host.

In addition, containers encourage a modular, loosely coupled architecture. Each container typically runs a single application or process and communicates with other containers over the network. This aligns well with modern microservices and cloud-native development patterns.

However, containers also have some limitations compared to VMs:

• Reduced isolation, as containers share the same kernel
• Difficulty running different OS distributions or kernels
• Potential dependency conflicts between containerized applications
• Added complexity of container orchestration and networking in production

So in practice, VMs and containers both have their place, and are often used together in a hybrid architecture. For example, you might provision VMs to run a Kubernetes cluster, which then manages deployments of containerized applications.

Enter Docker

While Linux containers have existed for over a decade, Docker made them accessible and attractive to a wide audience of developers. Docker is an open-source platform that automates the deployment, scaling, and management of containers.

At its foundation is the Docker Engine, which is a daemon process that manages the container lifecycle. The engine exposes a REST API that can be driven by the Docker CLI tool or other programs. You can think of containers as a client-server architecture, where the CLI sends commands to the Engine to build, ship, and run containers.

Docker also provides other key components:

• Docker Registry stores and distributes container images
• Docker Compose defines and runs multi-container applications
• Docker Swarm clusters multiple Docker hosts and schedules containers
• Docker Machine provisions new Docker hosts, e.g. in the cloud

While Docker didn‘t invent containers, it was the first tool to combine these pieces into a complete platform with a great developer experience. Let‘s look closer at some of the core concepts.

Packaging Apps in Docker Images

To run an application in Docker, you first need to package it as an image. An image is an immutable file that includes everything needed to run the application: code, config, dependencies, etc. You define an image using a Dockerfile, which specifies the base OS, along with the commands to install packages, copy files, and set environment variables, among other steps.

Here‘s an example Dockerfile for a simple Python app:

FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .  
CMD ["python", "app.py"]

To build the image, you run docker build and provide a tag:

docker build -t myapp:v1 .

The resulting image is stored on your local machine. You can list images with docker images and inspect details with docker inspect.

Images are made up of stacked read-only layers, where each layer corresponds to an instruction in the Dockerfile. Layers are cached, so if you rebuild the image after changing a line in the Dockerfile, Docker will reuse the layers up to that line. This makes builds very fast and efficient.

To share an image with others, you need to push it to a registry. Docker Hub is the default public registry where you can host free public repositories. Many open-source images are published on Docker Hub, such as official images for Ubuntu, MySQL, and Node.js to name a few. You can also run a private registry to distribute images within your organization.

Running Containers from Images

Once you have an image, you can start one or more containers from it. A container is a runnable instance of an image. You can think of an image as a class, and a container an object of that class. When you run docker run, behind the scenes Docker creates a writeable container layer and starts the process specified in the image‘s CMD.

docker run -d -p 8080:80 --name web myapp:v1  

This command starts a container from the myapp:v1 image in detached mode, maps port 8080 on the host to port 80 in the container, and sets the container name to web. You can view running containers with docker ps, attach to a container to view its output with docker attach, and execute a command inside a running container with docker exec.

Containers have an isolated view of the host filesystem. When a container writes files, the data is stored in the writeable container layer. Multiple containers started from the same image will not see each other‘s filesystem changes. When the container is removed, by default its data is lost. To persist data, you can use Docker volumes.

Persisting Data with Volumes

A volume is a directory in the host filesystem that is mounted into a container. Volumes outlive containers and allow storing data outside the Union File System. There are three main types of volumes:

1. Host volumes mount a directory from the host, specified by an absolute path
2. Anonymous volumes let Docker create a directory somewhere on the host and manage its lifecycle
3. Named volumes also let Docker manage storage, but provide a name for the volume that containers can reference

For example, to mount the host‘s /data directory into a MySQL container at /var/lib/mysql, you would run:

docker run -v /data:/var/lib/mysql mysql

Volumes solve several problems with container storage:

• Data can persist even after the container is deleted
• A volume can be mounted into multiple containers, allowing them to share data
• Host data can be exposed to containers in a controlled way
• Volumes can be managed and backed up independently from containers

Connecting Containers with Networks

By default, containers run in isolation and do not know anything about other processes or containers on the same machine. To allow containers to communicate, you need to explicitly specify how to connect them using Docker networks.

When you install Docker, it creates three networks by default:

1. Bridge: The default network containers connect to if no other network is specified. Containers on the bridge can access each other by IP address.

2. Host: Removes network isolation and uses the host‘s network stack directly. The container‘s network is now the same as the host.

3. None: Disables all networking for the container.

You can create your own bridge networks to isolate containers for different applications or environments:

docker network create mynet
docker run -d --net mynet --name db postgres
docker run -d --net mynet --name web myapp 

Here we create a custom bridge network called mynet, start a Postgres container called db, and then start a web container from the myapp image. The web container will be able to access the db container using the hostname ‘db‘.

Docker also supports other network modes, such as overlays for multi-host networking, and integration with third-party network plugins. Configuring Docker networks properly is key for building scalable and secure container deployments.

Containerizing an Application with Docker

Now that we‘ve covered the key concepts, let‘s walk through an example of containerizing a web app with a database backend. We‘ll use Docker Compose to define and run this multi-container application.

First, we create a Dockerfile for our app:

FROM node:14-alpine
WORKDIR /app
COPY package.json yarn.lock ./
RUN yarn install
COPY . .
EXPOSE 3000  
CMD ["yarn", "start"]

Next, we define our services in a docker-compose.yml file:

version: "3.8"
services:
  web:
    build: .
    ports:
      - "3000:3000"
    depends_on:
      - db  
  db:
    image: postgres:13-alpine 
    volumes:
      - db-data:/var/lib/postgresql/data
    environment:
      POSTGRES_PASSWORD: secret
volumes:
  db-data:

This Compose file defines two services: web and db. The web service builds an image from the Dockerfile in the current directory, maps port 3000 in the container to 3000 on the host, and depends on the db service. The db service uses the official postgres image, mounts a named volume to persist data, and sets an environment variable for the database password.

To start the application, simply run:

docker-compose up -d

Docker Compose will build the web image, create a network, start the containers, and set up the dependencies between them. Your multi-container app is now running! You can view logs with docker-compose logs and tear everything down with docker-compose down.

Using Docker for Development and Deployment

Composing containers like this is extremely useful for development. You can define your entire application stack, including databases, message queues, and other backing services, in one declarative file. Developers can check out the code and spin up their environments with a single command, without installing any dependencies locally besides Docker. This eliminates the "works on my machine" problem and makes onboarding new devs much smoother.

Furthermore, the same images built locally can be deployed to production with minimal changes. By shipping your application as a container, you abstract away differences in OS distributions, dependency versions, and runtime environments. Containers provide a consistent interface between dev, staging, and production.

Of course, running containerized workloads in production comes with its own set of challenges. You need to consider things like:

• How to deploy containers to a cluster of hosts for high availability
• How to update applications with zero downtime
• How to monitor container health and resource utilization
• How to scale services up or down in response to load
• How to configure networking, service discovery, and load balancing
• How to secure access to sensitive data and services

To address these concerns, most organizations use a container orchestration platform such as Kubernetes, Docker Swarm, or Amazon ECS. These tools provide a declarative way to manage containerized applications across a cluster, with features like auto-scaling, self-healing, rolling updates, and service mesh. But that‘s a topic for another article!

Conclusion

In this article, we‘ve covered a lot of ground: from the fundamentals of virtualization and containerization, to packaging and running applications with Docker, to composing multi-service applications and deploying them in production. While Docker has some alternatives like Podman and Buildah, it remains the most popular and widely-supported container platform.

We‘ve seen how Docker improves the developer experience, saves resources, and enables a more agile and portable way of building, shipping, and running distributed applications. However, we‘ve also noted that VMs are still useful in many scenarios, and often used in conjunction with containers.

If you‘re just getting started with Docker, I recommend diving into the official Getting Started guide and walking through the hands-on tutorials. There are also countless blog posts, conference talks, and books that go deeper on every aspect we‘ve touched on here.

Looking ahead, the cloud-native ecosystem is rapidly evolving, with new standards like OCI and CRI-O emerging to improve container security and interoperability. Exciting projects like WebAssembly and Unikernels promise even greater portability and performance for containerized workloads. One thing is clear: the future of software development and deployment is bright, and Docker will no doubt continue to play a major role. Happy containering!