What Is Containerization? Everything You Need to Know

It’s time to put on the guise of Dennis Ritchie, one of the greatest programmers of all time. Let’s say you wrote a revolutionary piece of software, and you want to make sure it runs seamlessly, no matter where it’s deployed.

Now, while your laptop is running Windows, one of your colleagues uses macOS, and the other uses Linux. When you try to run the software on their computers, it breaks. Maybe a missing library or a different software version is the issue.

This is where containerization comes in handy. Instead of relying on the individual setups of machines to run your code, you can package your code and everything it needs to run into a lightweight container.

Containerization is when you package your code and dependencies into a single “container.” This allows your code to run on any machine or operating system and enhances efficiency, portability, and security.

With containerization, you can run your container on any machine — you’re not just shipping the code with a container, you’re shipping the entire environment. You may be thinking, “Isn’t virtualization a better solution?”

To answer your question in short: yes and no. The world of containers is a fascinating one, and I promise to plunge into the shallow waters of virtualization as well. Pack your bags; May the trip begin!

For starters, you need to know the different dependencies that a container packages with your code to ensure it runs consistently across different environments.

These include:

• Libraries
• Runtime environments and frameworks
• Configuration files
• Environment variables
• Tools and utilities
• Operating system dependencies
• Network configuration
• External services configuration

Unlike virtual machines (VMs), which run their own operating systems (OSes), containers share the host’s operating system kernel with other containers — containers provide process-level isolation.

Now, when I said you can run your container anywhere, what I meant was it should work if the host OS kernel matches what the container expects. For example, a Linux container expects a Linux kernel, and a Windows container needs a Windows kernel.

This is where containerization tools like Docker, which is the most widely used containerization platform, “cheat” a little.

Let’s say you want to run a Linux container on your Windows laptop. Docker will spin up a virtual machine instance in the background that mimics the Linux environment the container needs to run.

Unlike traditional virtual machines, these VMs are much lighter and more efficient — they don’t consume a lot of resources, so don’t worry!

Note: All of your containers within the VM are isolated from the host system and from containers outside the VM. They can only interact with each other within the VM and are isolated at the process level.

Key Components

I’ve already talked about the role of the operating system in containerization, so without wasting any time, let’s explore its other key components: container runtime and container images.

If you love cars, you’ll appreciate the role of container runtime in containerization — it’s basically the engine that fuels your containers.

From initializing containers to managing their resources and shutting them down when they’re no longer needed, container runtime is like a 1,341 HP V8 engine.

You can think of container images as blueprints for your containers — they define everything your application needs to run. Once you create a container image, it’s typically stored in a container registry.

When you want to run the app, the container runtime takes the image from the registry and turns it into a running container. Take extra care when you’re creating a container image for your app, as you can’t update its settings afterward — you’ll have to create a new image instead.

I used to code when I was in Uni, and I’ve faced the classic “it works on my machine” problem. Containerization tosses this problem out of the window and is a blessing in disguise for developers.

By “disguise”, I mean containerization is like a magic trick for your applications — the “magic” behind containerization isn’t sleight of hand, but the power of efficiency, consistency, and simplicity. Big words, right? Just hear me out.

Portability

A container is as portable as the lunchbox your mother used to pack for you in primary school.

With containers, you can run your applications on any machine without hiccups, be it your laptop, a test server, or a cloud-based environment.

You can confidently deploy your apps across development, testing, and production environments and expect consistent behavior since the underlying environment is the same everywhere.

Efficiency

You see, containers and virtual machines have unique strengths and use cases, so it’s unfair to say one is better than the other.

When it comes to efficiency, though, containers come out on top, and this isn’t up for debate.

They’re lightweight compared to virtual machines, allowing for faster startup times and reduced resource usage.

This also means you can run more containers on the same piece of hardware.

Of course, you should be diligent when creating containers — you know what they say about too much of a good thing.

Scalability

Now, this is one of the coolest applications of containers.

Using container orchestration tools like Kubernetes, you can automate the dynamic scaling of containers in response to the demand for your application.

When the demand drops, Kubernetes just as easily scales back resources.

This flexibility ensures your app is always running optimally without wasting resources.

If you plan on launching a full-fledged eCommerce store, for example, you need Kubernetes.

Flexibility and Isolation

What does process-level isolation really mean?

Well, it means that the processes running within one container are unaffected by the processes within another.

Basically, each container runs independently, making it easier to manage dependencies. Also, if one fails, the others will remain unaffected.

So, if it suits you, you can tailor containers for specific dependencies and configurations and run them on the same machine without worrying about them interfering with each other.

If you’re implementing a microservices architecture, containerization is practically your default option. Companies like Netflix, Spotify, and Uber use containerization to help them efficiently manage, scale, and deploy hundreds of microservices.

But that’s not the only use case for containerization — it’s used almost everywhere. Let me first explain what microservices are, to give you some context!

Microservices Architecture

If you enjoy Indian cuisine, you must try a thali — a meal that consists of more than 10 Indian dishes. These dishes are typically served in small bowls, called katoris, and arranged on a round, metal platter, called a thali.

Can you imagine how messy things would get if these dishes were assembled directly on the thali? I’d hate for rice grains to touch my chapatis!

Similarly, things could get out of hand if you manage your entire codebase as a single unit. On the other hand, if you divide them into microservices hosted in “katoris” (containers), things will get much easier.

Plus, if you want to accommodate another dish on your “thali”, you can simply spin up a new katori!

CI/CD Pipelines

A Continuous Integration (CI) and Continuous Delivery/Continuous Deployment (CD) pipeline is like an automated assembly line for code.

Essentially, whenever you write a piece of code, the CI/CD process kicks in to check whether it integrates well with the rest of the codebase. If it passes all the tests, it gets pushed live. Remember the “it works on my machine” issue I talked about earlier?

Containerization is a godsend for CI/CD pipelines, as it ensures your development, testing, and production environments are identical — so your code works the same everywhere.

Cloud-Native Applications

What are the three things cloud-native applications are designed to be? Scalable, flexible, and easily manageable. You know where I’m going with this — containers help you achieve all that.

Containers help your cloud-native applications achieve scalability, flexibility, and ease of management.

By packing your cloud-native app into containers, you can run it on any cloud provider. And when I say any, I mean any — whether it’s Amazon Web Services (AWS), Google Cloud, or Azure — containers are generally cloud-agnostic.

Application Modernization

If you have legacy applications that you just can’t get rid of (for the time being, hopefully), you can turn to containers to improve their scalability, speed of deployment, and ability to run across different environments.

How, you ask? You can wrap these legacy apps into containers and move them to the cloud. This way, you can get more out of your existing software — talk about making lemonade from lemons!

I could talk endlessly about the differences between containerization and virtualization, but that would be a waste of time. I mean, why type paragraphs when a simple table can paint a picture that speaks a thousand words?

Here’s all you need to know about the differences between the two technologies:

In my opinion, containerization is hands down better than virtualization.

But then again, if you’re building an application where security, compliance, and legacy systems matter (like in the financial and healthcare sectors), virtual machines can be a better fit (don’t worry, you can still implement a microservices architecture).

I’ve already listed the use cases for containers, so I’ll rest my case.

I’m sure you’ve heard of Kubernetes and Docker at some point. I used to think Kubernetes and Docker were like the Lionel Messi and Cristiano Ronaldo of the containerization world.

Turns out, while they’re indeed GOATs, they’re more like Lionel Messi and Luis Suarez — they ball on the same team.

Docker

It’s no secret that Docker is the most commonly used containerization platform. If you haven’t connected the dots already, Docker is a container runtime tool.

In other words, you can use it to package apps and their dependencies into Docker images, which can then be turned into containers.

You can also use the tool to manage individual containers on a single machine. The keywords here are a “single machine”. So if you’re working with a few containers on a single, local machine, Docker is the man for the job.

Kubernetes

Docker won’t cut it if you need to manage large fleets of containers. In this case, you need a container orchestration platform like Kubernetes to do the dirty work for you.

Yes, you heard right — Kubernetes automates container management at scale across multiple machines. All you have to do is configure things initially and Kubernetes take over the reins.

Kubernetes also offers advanced features such as auto-scaling, load balancing, self-healing, rolling updates, and service discovery. And yes, both Docker and Kubernetes are open-source software, so cost is no barrier. Mind you, Kubernetes is simply a container orchestration platform. You still need to pair it with a container runtime solution like Docker.

Other Popular Tools

Talking of container runtime tools, it’s not written in stone that you have to opt for the combination of Kubernetes and Docker.

In fact, container runtimes like containerd and CRI-O are more lightweight and efficient. Docker was once the default Kubernetes container runtime, but this is no longer the case.

Docker does provide additional features for development and building images, though. Also, instead of using Kubernetes, you could use Docker with Docker Swarm, Docker’s native container orchestration tool. I’d do my research if I were you!

When you went through the table I drew up to illustrate the differences between containerization and virtualization, one thing in particular must have struck you: containerization has “weaker isolation” than virtualization.

And that’s not the only challenge you will face when you implement containerization. Virtualization has its own set of hurdles, so don’t jump ship just yet. Having a better understanding of these challenges will help you in the short and long run.

Security Concerns

Your containers share the same operating system kernel environments, which could expose all your containers to vulnerabilities.

Before you start panicking, let me explain: if an attacker gains access to one container, they could escalate privileges and compromise the host or other containers.

Additionally, if container images aren’t properly vetted, they could carry malicious code that could exploit weaknesses in container runtimes or orchestration platforms.

You must do all you can to safeguard against these risks, starting with implementing strong image scanning, proper privilege management, and comprehensive monitoring strategies.

Complexity in Orchestration

It’s nearly impossible for you to manage hundreds, if not thousands, of containers for large-sized containerized applications, without an orchestration platform like Kubernetes.

It would take a superhuman feat, and I’m certain even Superman would find this difficult (before throwing shade, he isn’t an IT expert anyway)! Even if you use Kubernetes for container orchestration, its learning curve is steep.

Put your head down, learn all you can, and ensure your orchestration plan is well-structured.

Data Management

You can think of containers as RAM in a way — they don’t store any permanent data themselves.

In other words, they’re “stateless”, which sounds harsh, to be honest!

Anyway, when a container is stopped or deleted, all data within it is lost. This isn’t ideal for apps that need to keep data across container restarts or when scaling up.

This is where you need to carefully plan and configure external storage solutions, such as cloud storage.

Resource Management

Kubernetes isn’t a magician — it only orchestrates containers based on the configurations you set for it. So if you expect it to magically spin up containers and handle resource management for them, you’re wrong.

You have to set resource requests and define limits to help Kubernetes effectively automate resource management. And that’s not all — you need to monitor performance and adjust configurations regularly.

I was really excited when I finally learned how to create a container using Docker — I’m more of a practice-over-theory guy. I hope you share this enthusiasm with me as we explore basic Docker code!

I’m going to keep things simple for you. For starters, let’s learn how to create a simple container using Docker.

1. No surprises here; Install Docker on your machine and set it up!
2. Open the command line interface (CLI) and enter this command to check if Docker is installed correctly: “docker –version”.
3. You should see the version of Docker that’s installed.
4. Next, Explore Docker Hub, Docker’s public container image library, and download an image to your local machine. For example, if you pull a basic Python image, you can spin up a container with this simple command: “docker run -it python:3.9 bash”.

That was easy, right? Let’s take things up a notch and learn how to build a custom container image. To create one, you’ll need to first write something called a Dockerfile.

Here’s a simple example:

“FROM python:3.9

WORKDIR /app

COPY . .

RUN pip install -r requirements.txt

CMD [“python”, “app.py”]”

You don’t need a special command to create a Dockerfile, as each one starts with the “FROM” instruction. In this example, the Dockerfile pulls your base Python image, copies your app files, installs dependencies, and specifies the command to run your app.

Now, you can build your image by running the command (it basically executes the instructions mentioned in the Dockerfile):

“docker build -t my-python-app .”.

Once you’ve built your image, you can run (as a container), stop, and remove it with these basic commands respectively:

• “docker run -d my-python-app”.
• “docker stop ”.
• “docker rm ”.

Here’s a final piece of information. If you’re running multiple containers, you can obtain a list of them using this command: “docker ps”. Happy building!

There’s no secret ingredient to success.

But judging by how many modern apps use a microservices architecture — or a hybrid blend where parts of the system are monolithic, and others are microservices — containerization is indeed an ingredient that could set you up for success.

Netflix is a classic example. It has more than 1,000 microservices that are bundled into containers to streamline deployment and manage scalability.

If you’re a budding entrepreneur or are looking to make the switch from a monolithic architecture to a microservices architecture, I’d study the Netflix model if I were in your shoes.

Uber, Spotify, Airbnb, Amazon, LinkedIn, Walmart, Slack, X (formerly Twitter), and Shopify are other apps I’d look at. From where I see things, if your app is built to service millions or, hopefully, one day, billions of users, it’s bound to be complex.

This is where containers step in to simplify things. So, to “wrap” things up, containers matter.