What Are Software Containers?

You’re probably familiar with containers if you’re even remotely involved in software development or systems administration. In their 2023 survey, the Cloud Native Computing Foundation found that over 90% organizations use containers in production. Additionally, more than 90% of organizations that rely on cloud native practices for most or all of their application development and deployment also depend on containers. 

But, whether orchestrating containers is a regular part of your day-to-day life, or you are just trying to understand what an operating system kernel is, it helps to have an understanding of some container basics.

Today, we’re explaining what containers are, how they’re used, and how cloud storage fits into the container picture—all in one neat and tidy containerized blog post package. (And, yes, the kernel is important, so we’ll get to that, too).

What are containers?

Containers are packaged units of software that contain all of the dependencies (e.g. binaries, libraries, programming language versions, etc.) they need to run no matter where they live—on a laptop, in the cloud, or in an on-premises data center. That’s a fairly technical definition, so you might be wondering, “OK, but what are they really?”

The generally accepted definition of the term applies almost exactly to what the technology does.

A container, generally = a receptacle for holding goods; a portable compartment in which freight is placed (as on a train or ship) for convenience of movement.

A container in software development = a figurative “receptacle” for holding software. The second part of the definition applies even better—shipping containers are often used as a metaphor to describe what containers do. In shipping, instead of stacking goods in a jumbled pile, goods are packed into standard-sized containers that fit on whatever is hauling them—a ship, a train, or a trailer.

Likewise, instead of “shipping” an unwieldy mess of code, including the required operating system, containers package software into lightweight units that share the same operating system (OS) kernel and can run anywhere—on a laptop, on a server, in the cloud, etc.

Compared to older virtualization technology, namely virtual machines which are measured in gigabytes, containers are only megabytes in size. That means you can run quite a few of them on a given computer or server much like you can stack many containers onto a ship.

Indeed, the founders of Docker, the software that sparked widespread container adoption, looked to the port of Oakland, California for inspiration. Former Docker CEO, Ben Golub, explained in an interview with InfoWorld, “We could see all the container ships coming into the port of Oakland, and we were talking about the value of the container in the world of shipping. The fact it was easier to ship a car from one side of the world than to take an app from one server to another, that seemed like a problem ripe for solving.” In fact, it’s right there in their logo.

And that is exactly what containers, mainly via Docker’s popularity, did—they solved the problem of environment inconsistency for developers. Before containers became widely used, moving software between environments meant things broke, a lot. If a developer wrote an app on their laptop, then moved it into a testing environment on a server, for example, everything had to be the same—same versions of the programming language, same permissions, same database access, etc. If not, you had a very sad app.

Virtualization 101

Containers work their magic by way of virtualization. Virtualization is the process of creating a simulated computing environment that’s abstracted from the physical computing hardware—essentially a computer-generated computer, also referred to as a software-defined computer.

The first virtualization technology to really take off was the virtual machine (VM). A VM sits atop a hypervisor—a lightweight software layer that allows multiple operating systems to run in tandem on the same hardware. VMs allow developers and system administrators to make the most of computing hardware. Before VMs, each application had to run on its own server, and it probably didn’t use the server’s full capacity. After VMs, you could use the same server to run multiple applications, increasing efficiency and lowering costs.

Containers vs. virtual machines

While VMs increase hardware efficiency, each VM requires its own OS and a virtualized copy of the underlying hardware. Because of this, VMs can take up a lot of system resources, and they’re slow to start up.

Containers, on the other hand, do not virtualize the hardware. Instead, they share the host operating system’s kernel, making them much smaller and faster than VMs. Want to know more? Check out our deep dive into the differences between VMs and containers.

The benefits of containers

Containers allow developers and system administrators to develop, test, and deploy software and applications faster and more efficiently than older virtualization technologies like VMs. The benefits of containers include:

1. Portability: Containers include all of the dependencies they need to run in any environment, provided that environment includes the appropriate OS. This reduces the errors and bugs that arise when moving applications between different environments, increasing portability.
2. Size: Containers share OS resources and don’t include their own OS image, making them lightweight—megabytes compared to VMs’ gigabytes. As such, one machine or server can support many containers.
3. Speed: Again, because they share OS resources and don’t include their own OS image, containers can be spun up in seconds compared to VMs which can take minutes to spin up.
4. Resource efficiency: Similar to VMs, containers allow developers to make the best use of hardware and software resources.
5. Isolation: Also similar to VMs, with containers, different applications or even component parts of a singular application can be isolated such that issues like excessive load or bugs on one don’t impact others.

Container use cases

Containers are nothing if not versatile, so they can be used for a wide variety of use cases. However, there are a few instances where containers are especially useful:

1. Enabling microservices architectures: Before containers, applications were typically built as all-in-one units or “monoliths.” With their portability and small size, containers changed that, ushering in the era of microservices architecture. Applications could be broken down into their component “services,” and each of those services could be built in its own container and run independently of the other parts of the application. For example, the code for your application’s search bar can be built separately from the code for your application’s shopping cart, then loosely coupled to work as one application.
2. Supporting modern development practices: Containers and the microservices architectures they enable paved the way for modern software development practices. With the ability to split applications into their component parts, each part could be developed, tested, and deployed independently. Thus, developers can build and deploy applications using modern development approaches like DevOps, continuous integration/continuous deployment (CI/CD), and agile development.
3. Facilitating hybrid cloud and multi-cloud approaches: Because of their portability, containers enable developers to utilize hybrid cloud and/or multi-cloud approaches. Containers allow applications to move easily between environments—from on-premises to the cloud or between different clouds.
4. Accelerating cloud migration or cloud-native development: Existing applications can be refactored using containers to make them easier to migrate to modern cloud environments. Containers also enable cloud-native development and deployment.

The role of software containers in AI application development

In addition to enabling microservices architectures and supporting modern development practices, containers play a role in AI application development. Their ability to provide consistent, reproducible environments makes them ideal for AI, where managing complex dependencies and ensuring uniform performance across different platforms are essential. 

AI projects often rely on specific versions of libraries, drivers, and runtimes, which can lead to compatibility issues and errors. Containers solve this problem by encapsulating all necessary dependencies, libraries, and runtime environments to provide a consistent and reproducible platform for AI development. This encapsulation ensures that AI models and applications run the same way, regardless of the underlying infrastructure and provides consistency from development through production. 

The portability of containers also offers advantages for deploying AI workloads across diverse environments. They can be easily moved between local development machines, on-premises servers, and cloud platforms without requiring code or configuration changes. This flexibility supports easy scalability of AI applications to meet changing demands—such as increased user loads or the need for more intensive data processing. 

Additionally, containers enable organizations to leverage the most cost effective and powerful computing resources available, whether it’s local hardware for testing and development or cloud-based GPU clusters for training large-scale models. This ability moves workloads efficiently across different environments and also supports hybrid and multi-cloud strategies to provide organizations with greater agility, while reducing costs and avoiding vendor lock-in.

Container tools

The two most widely recognized container tools are Docker and Kubernetes. They’re not the only options out there, but in their 2023 developer survey, Stack Overflow found that nearly 52% out of 90,000+ respondents use Docker and 19% use Kubernetes. But what do they do?

1. What is Docker?

Container technology had been around for a while in the form of Linux containers or LXC, but the widespread adoption of containers happened only in the past decade with the introduction of Docker.

Docker was launched in 2013 as a project to build single-application LXC containers, introducing several changes to LXC that make containers more portable and flexible to use. It later morphed into its own container runtime environment. At a high level, Docker is a Linux utility that can efficiently create, ship, and run containers.

Docker introduced more standardization to containers than previous technologies and focused on developers, specifically, making it the de facto standard in the developer world for application development.

2. What is Kubernetes?

As containerization took off, many early adopters found themselves facing a new problem: how to manage a whole bunch of containers. Enter: Kubernetes. Kubernetes is an open-source container orchestrator. It was developed at Google (deploying billions of containers per week is no small task) as a “minimum viable product” version of their original cluster orchestrator, ominously named Borg. Today, it is managed by the Cloud Native Computing Foundation, and it helps automate management of containers including provisioning, load balancing, basic health checks, and scheduling.

Kubernetes allows developers to describe the desired state of a container deployment using YAML files (YAML stands for Yet Another Markup Language, which is yet another winning tech acronym.). The YAML file uses declarative language to tell Kubernetes “this is what this container deployment should look like” and Kubernetes does all the grunt work of creating and maintaining that state.

Containers + storage: What you need to know

Containers are inherently ephemeral or stateless. They get spun up, and they do their thing. When they get spun down, any data that was created while they were running is destroyed with them. But most applications are stateful, and need data to live on even after a given container goes away.

Object storage is inherently scalable. It enables the storage of massive amounts of unstructured data while still maintaining easy data accessibility. For containerized applications that depend on data scalability and accessibility, it’s an ideal solution for keeping stateful data stateful.

There are three essential use cases where object storage works hand in hand with containerized applications:

1. Backup and disaster recovery: Tools like Docker and Kubernetes enable easy replication of containers, but replication doesn’t replace traditional backup and disaster recovery just as sync services aren’t a good replacement for backing up the data on your laptop, for example. With object storage, you can replicate your entire environment and back it up to the cloud. There’s just one catch: some object storage providers have retention minimums, sometimes up to 90 days. If you’re experimenting and iterating on your container architecture, or if you use CI/CD methods, your environment is constantly changing. With retention minimums, that means you might be paying for previous iterations much longer than you want to. (Shameless plug: Backblaze B2 Cloud Storage is calculated hourly, with no minimum retention requirement.)
2. Primary storage: You can use a cloud object storage repository to store your container images, then when you want to deploy them, you can pull them into the compute service of your choice.
3. Origin storage: If you’re serving out high volumes of media, or even if you’re just hosting a simple website, object storage can serve as your origin store coupled with a CDN for serving out content globally. For example, CloudSpot, a SaaS platform that serves professional photographers, moved to a Kubernetes cluster environment and connected it to their origin store in Backblaze B2, where they now keep 120+ million files readily accessible for their customers.

Need object storage for your containerized application?

Now that you have a handle on what containers are and what they can do, you can make decisions about how to build your applications or structure your internal systems. Whether you’re contemplating moving your application to the cloud, adopting a hybrid or multi-cloud approach, or going completely cloud native, containers can help you get there. And with object storage, you have a data repository that can keep up with your containerized workloads.

Ready to connect your application to scalable, S3-compatible object storage? You can get started today for free.