Docker vs. Virtual Machines: Key Differences

1. Introduction

1.1 Goals of the Tutorial

This tutorial aims to provide an in-depth comparison between Docker and Virtual Machines. It will highlight their key differences and help you understand when to use each technology.

1.2 What You Will Learn

By the end of this tutorial, you will have a clear understanding of Docker and Virtual Machines, their differences, and the use cases for each. You will also gain practical knowledge through code examples and exercises.

1.3 Prerequisites

There are no specific prerequisites for this tutorial. However, familiarity with Linux and basic programming concepts will be beneficial.

2. Step-by-Step Guide

2.1 Docker

Docker is an open-source platform that uses containerization technology to package and run applications with their dependencies isolated on a single operating system.

Example

Consider a scenario where you have an application that requires Python 3.7, but your system has Python 2.7 installed. Docker can create a container with Python 3.7 for your application, without affecting the system's Python version.

2.2 Virtual Machines

Virtual Machines (VMs) simulate a physical computer. They run an entire operating system, which can be different from the host operating system. VMs provide an isolated environment for running applications.

Example

If you have a Windows machine and want to run a Linux program, you can create a Linux VM on your Windows machine and run the program inside the VM.

2.3 Key Differences

Docker containers are lightweight because they share the host system's kernel, while VMs are heavier as they need to run a full operating system. Docker containers start faster than VMs. However, VMs provide more isolation than Docker containers.

3. Code Examples

3.1 Docker Example

Here's a simple Dockerfile to run a Python script:

# Dockerfile
FROM python:3.7
COPY . /app
WORKDIR /app
RUN pip install -r requirements.txt
CMD ["python", "app.py"]

This Dockerfile tells Docker to:

• Use the Python 3.7 image (FROM python:3.7)
• Copy the current directory into the container (COPY . /app)
• Set the working directory to /app (WORKDIR /app)
• Install the Python dependencies (RUN pip install -r requirements.txt)
• Run app.py when the container starts (CMD ["python", "app.py"])

After building and running this Docker container, it executes the app.py script.

3.2 Virtual Machine Example

Here's a command to create a Linux VM using VirtualBox:

VBoxManage createvm --name "LinuxVM" --ostype Ubuntu_64 --register

This command tells VirtualBox to:

• Create a new VM (createvm)
• Name the VM "LinuxVM" (--name "LinuxVM")
• Set the operating system type to Ubuntu 64-bit (--ostype Ubuntu_64)
• Register the VM with VirtualBox (--register)

After running this command, you can start the VM and run your Linux application inside it.

4. Summary

In this tutorial, we compared Docker and Virtual Machines, highlighting their key differences. Docker uses containerization to provide a lightweight and fast solution for running applications with their dependencies. On the other hand, VMs offer more isolation by running a full operating system, but they are heavier and slower than Docker containers.

5. Practice Exercises

Exercise 1

Create a Dockerfile to run a Node.js application.

Exercise 2

Create a Windows VM using VirtualBox and install Python on it.

Exercise 3

Compare the startup times of a Docker container and a VM.

For more practice, try using Docker and VMs in different scenarios and compare their performance and ease of use. Happy learning!