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What is container orchestration? Learn when and when not to use containers in this complete guide on container orchestration and its benefits.
Containerization is gaining traction across nearly all industries and company sizes worldwide. In fact, the 2019 edition of Portworx’s annual Containers Adoption Survey report showed over 87% of surveyed organizations were using container technologies. Over 90% of them use containerization in production.
In 2021, the Kubernetes Adoption Report showed 68% of surveyed IT professionals increased their adoption of containers during the pandemic. Among their goals were speeding up deployment cycles, increasing automation, reducing IT costs, and developing and testing artificial intelligence (AI) apps and models.
What role do container technologies play in this?
In this guide, we’ll cover what containers are and how container orchestration works. We'll also discuss container benefits and see if you are missing out before showing you how to take full advantage of containers and container orchestration.
Table Of Contents
A container is an executable unit of software that helps package and run software code, libraries, dependencies, and other parts of an application so that it can work reliably in different computing environments.
Containerized apps can run as smoothly on a local desktop as they would on a cloud platform or portable laptop. Containerization is the process of developing, packaging, and deploying applications in containers.
Engineers can containerize separate parts of an app in a container. They can also contain an entire app.
Containers leverage a form of virtualization technology to accomplish this level of portability, performance, and consistency across varying environments.
In a nutshell, virtualization is the act of using a single computer's hardware to create multiple virtual computers. They can then use separate operating systems to perform different computing tasks on top of a single physical server.
Containers sit on top of the host server's hardware, allowing multiple containers to share the server's OS. The containers share the OS kernel, as well as libraries, binaries, and different software dependencies.
That means containers offer several benefits.
Containerization is simpler to digest with a visual scenario.
Imagine a loaded ship about to dock in a port.
It is stacking hundreds or thousands of shipping containers. Each container contains a unique set of goods. Together, the containers make up the cargo.
It’s easy to stack cargo containers on an ocean-going vessel and transport them when they are correctly loaded. Containers can withstand substantial storms along the way without destabilizing the ship or collapsing into the ocean.
Now picture this.
As shipping containers hold goods, computing containers hold application code, its libraries, and dependencies. There can be a few to thousands of containers supporting a single application. An application remains stable and performs well under varying computing loads with proper containerization.
Cargo containers are typically easy to move from one ship to another or a train because they have standard designs which allow for effortless lifting, stacking, tracking, and offloading. Likewise, computing containers are designed to be moved from one computing environment to another with minimal to no changes to their architecture.
Containers just “attach” to the host operating system and start working as expected.
You can open and replace the components of a single cargo container without affecting other containers. That's similar to how a software engineer can change the code of a computing container without affecting the rest of the app.
Just as easily as moving cargo containers to another transportation mode, you can move an app's building blocks (code, binaries, libraries, and dependencies) to another computing environment using containers, and it will continue to work as usual. You can still make minor adjustments to optimize performance or security in the new environment.
If a ship runs into trouble in the Suez Canal, such as a fire in one of the containers, the crew can isolate the container in question to extinguish the fire before it lights up and sinks the ship. After that, the crew can inspect the damaged cargo, offload it, and replace it with a new load to continue on its route — an efficient disaster recovery operation.
Similarly, if you notice problems with your app, engineers can swiftly jump to action, isolate the problem in some containers, and update their code to correct it.
Containers refer to software packages that contain everything an application unit needs to function. Microservices refers to the technology that makes it possible to split up a large (monolithic) application into smaller, multiple services, each performing a specific function.
Remember how we defined containers as packages with application code, binaries, dependencies, and more within them? Think of microservices as the goods in a shipping container and containers as, well, cargo containers. You put microservices inside containers.
Microservices architecture allows software engineers to turn monolithic applications into multiple units that are easier to auto-scale, refactor individually, and faster to deploy, patch problems, and do disaster recovery.
Netflix is an excellent example of how to use microservices to achieve these goals.
Containers mount on top of a physical server's hardware, sharing a single operating system. At the same time, virtual machines (VM) use software, firmware, or hardware to create multiple virtual machines running different operating systems on top of a single host.
Many people confuse virtual machines with containers because they are both forms of virtualization.
Here’s an in-depth look at how both are similar yet different.
Comprehending what containers, containerization, and container orchestration are helps to understand why software engineers invented containers decades ago. Although the processing power of servers had increased over the years, bare metal apps were unable to tap into these developments to improve performance.
This challenge led engineers to imagine running software atop a physical server that would help tap into the abundance of resources they were seeing.
That is how virtual machines (VMs) were born. Engineers could sit a hypervisor (hardware, firmware, or software that creates, runs, and monitors VMs) on top of a physical server’s hardware to produce several virtual computers. That process is now popularly known as virtualization.
Virtualization lets you run several operating systems on the same hardware. Each VM can run its operating system (a guest OS). That way, each VM can service different applications, libraries, and binaries from the ones next to it.
VMs enable engineers to run numerous applications with ideal OSs on a single physical server to increase processing power, reduce hardware costs, and reduce operational footprint. They no longer need to run a single application per entire server. This frees up computation resources for use elsewhere other than development.
But VMs are not perfect. Because each VM runs an OS image, binaries, and libraries within it, it can gain weight quickly, turning into several gigabytes-heavy fast.
VMs typically take minutes instead of seconds to start. That is a performance bottleneck because minutes add up to hours when running complex applications and disaster recovery efforts.
VMs also have trouble running software smoothly when moved from one computing environment to another. This can be limiting in an age where users switch through devices to access services from anywhere and anytime.
As discussed earlier, containers are lightweight, share a host server’s resources, and, more uniquely, are designed to work in any environment — from on-premise to cloud to local machines.
Here’s an image showing the design difference between containers and virtual machines:
Now here is the full extent of the differences between traditional deployment vs. virtualization vs. containerization in one image.
So, what are containers used for?
Organizations use containers for a variety of reasons. The following are several uses of containers in cloud computing.
With a clear picture of what containers are, what they do, and containers use cases in mind, understanding container orchestration will not feel so overwhelming.
Container orchestration is the automated process of coordinating and organizing all aspects of individual containers, their functions, and their dynamic environments. Container deployment and scaling, networking, and maintenance are all aspects of orchestrating containers.
A single application can have hundreds of containers. The number of containers you use could be thousands if you use microservices-based applications.
Managing all of these containers manually is challenging. So DevOps engineers use automation to ease and optimize container orchestration.
Using container orchestration, engineers can manage when and how containers start and stop, schedule and coordinate components' activities, monitor health, distribute updates, and institute failover and recovery processes.
Engineers who work in DevOps cultures use container orchestration platforms and tools to automate that process throughout the lifecycle of containers.
Modern orchestration tools use declarative programming to ease container deployments and management. This is different from using imperative language. The declarative approach lets engineers define the desired outcome without feeding the tool with the step-by-step details of how to do it.
Think about how you order an Uber.
You do not need to instruct the driver how to drive his car, what shortcuts to take, and how to get to a particular destination. You just tell them you are in a hurry and, well, that you need to arrive at your destination in one piece. They know what to do next.
In contrast, an imperative approach requires engineers to give detailed instructions on how to orchestrate containers to accomplish a specific goal. This increases complexity, making containers deployed in this way less advantageous over virtual machines after all.
Using our Uber analogy, an imperative approach would be similar to taking a ride to an unfamiliar destination the driver is unfamiliar with. It is crucial that you know precisely how to get there and clearly explain all the turns and shortcuts to the driver, or else you may get lost in an unfamiliar neighborhood.
Orchestrating containers has various uses, including:
Orchestration simplifies container management. In addition, orchestration tools help determine which hosts are the best matches for specific pods. That further eases your engineers’ job while reducing human error and time used. Orchestrating also promotes optimal resource usage.
If a failure occurs somewhere in that complexity, popular orchestration tools restart containers or replace them to increase your system's resilience.
Now, let’s talk about container orchestration tools or platforms.
Several container orchestrators are available on the market today.
The following are the top four container orchestrators.
Kubernetes is an open-source container orchestration platform that supports both declarative automation and configuration. It is the most common container orchestrator today. Google originally developed it before handing it over to the Cloud Native Computing Foundation.
Kubernetes orchestrates containers using YAML and JSON files. It also introduces the notion of pods, nodes, and clusters.
What are the differences between pods, nodes, clusters, and containers?
Some container orchestration platforms do not run containers directly. Instead, they wrap one or more containers into a structure known as pods. Within the same pod, containers can share the local network (and IP address) and resources while still maintaining isolation from containers in other pods.
Since pods are a replication unit in the orchestration platform, they scale up and down as a unit, meaning all the containers within them scale accordingly, regardless of their individual needs.
A node represents a single machine, the smallest computing hardware unit that pod instances run on. When several nodes pull resources together, they make up a cluster, the master machine.
Several Kubernetes-as-a-Service providers are built on top of the Kubernetes platform.
Booking.com is one example of a brand that uses Kubernetes to support automated deployments and scaling for its massive web services needs.
While Kubernetes' extensive nature can make it challenging to manage and allocate storage, it can also expose your containerized apps to security issues if one container is compromised.
Docker Swarm is also a fully integrated and open-source container orchestration tool for packaging and running applications as containers, deploying them, and even locating container images from other hosts.
The Docker container orchestration method came in 2003, almost ten years before Kubernetes arrived. It popularized orchestrating containers for organizations that wanted to move away from using virtual machines.
It is ideal for organizations who prefer a less complex orchestrator than Kubernetes for smaller applications.
But it also integrates with Kubernetes in its Enterprise Edition if you want the best of both worlds.
A challenge with Docker is it runs on virtual machines outside the Linux platform (i.e., Windows and MacOSX). It can also have issues when you want to link containers to storage. Adobe, PayPal, Netflix, AT&T, Target, Snowflake, Stripe, and Verizon are among the enterprises that use Docker.
The University of California at Berkeley originally developed Mesos.
Apache Mesos offers an easy-to-scale (up to 10,000 nodes), lightweight, high-availability, and cross-platform orchestration platform. It runs on Linux, Windows, and OSX, and its APIs support several popular languages such as Java, Python, and C++.
Mesos offers only cluster-level management, unlike Kubernetes and Docker Swarm. However, Marathon provides container orchestration as a feature. It is also ideal for large enterprises as it might be overkill for smaller organizations with leaner IT budgets.
Uber, PayPal, Twitter, and Airbnb are some brands that use the Mesos container orchestration platform.
Like the others here, Nomad is an open-source workload orchestration tool for deploying and managing containers and non-containerized apps across clouds and on-premises environments at scale.
You can use Nomad as a Kubernetes alternative or a Kubernetes supplement, depending on your skills and application complexity. It is a simple and stable platform that is ideal for both small and enterprise uses. Cloudflare, Internet Archive, and Navi are some of the brands that use Nomad.
You can deploy and manage containerized apps at scale with containers. A container orchestration platform can help you do this with greater precision and automatically reduce errors and costs. It can also help you provide a reliable service to your users.
While orchestration tools offer the benefit of automation, many organizations struggle to connect container orchestration benefits to business outcomes.
You can also access real-time insights into pod and cluster costs relating to your organization’s features, products, and teams. This way, you can tell where to optimize costs or review your service pricing to remain profitable. to find out how CloudZero can help your engineering team measure, monitor, and optimize your Kubernetes costs in AWS