3 1 6 Containers
Summary
TLDRThis video script discusses containerization, a technology that packages application code with its libraries and dependencies into executable units that can run on any platform. It contrasts containers with virtual machines, highlighting their smaller size, faster deployment, and portability without the need for a guest OS. The script explains the history of container technology since Linux kernel's introduction of control groups in 2008 and how it enables agile DevOps practices by avoiding 'works on my machine' issues. It also illustrates the container creation process, from manifest to image to container, and shows how containers are more resource-efficient and scalable compared to VMs.
Takeaways
- 📦 **Containers Defined**: Containers are a unit of software that packages an application with its libraries and dependencies, allowing it to run consistently across different environments.
- 🚀 **Efficiency**: Containers are smaller and faster than virtual machines, as they do not require a guest OS, instead leveraging the host OS's features and resources.
- 🌐 **Portability**: Containers are designed to be portable, ensuring that applications run the same way on a desktop, in traditional IT, or in the cloud.
- 📚 **Containerization History**: The foundation of modern container technologies was laid in 2008 with the introduction of Linux control groups, which led to tools like Docker and Kubernetes.
- 🛠️ **Development to Production**: The script uses a node.js application as an example to highlight the benefits of containerization in moving applications from development to production.
- 🖥️ **Virtual Machines (VMs)**: VMs require a guest OS and additional libraries, leading to larger file sizes and resource consumption compared to containers.
- 🔄 **Scalability**: Containers are easier to scale due to their lightweight nature; they do not need to duplicate OS dependencies for each instance.
- 🔧 **Container Process**: The process of containerization typically involves creating a manifest (like a Dockerfile), building the image, and then running the container.
- 🔀 **Modularity**: Containers allow for modular deployment, where different applications can be scaled independently without being tied to the same VM.
- 🔄 **Resource Sharing**: Container technologies enable resource sharing among different processes, making efficient use of available hardware resources.
- 🔄 **Agility**: Containerization supports agile DevOps practices by simplifying the development, deployment, and scaling of cloud-native applications.
Q & A
What is a container in the context of software?
-A container is an executable unit of software that packages application code along with its libraries and dependencies, allowing it to run consistently across different environments like desktop, traditional IT, or the cloud.
How do containers differ from virtual machines?
-Containers are lightweight and do not require a guest OS in each instance. Instead, they leverage the features and resources of the host OS, unlike virtual machines that require a full guest OS for each instance.
When was the foundation for modern container technology introduced?
-The foundation for modern container technology was introduced in 2008 with the Linux kernel's C groups and control groups.
What are some examples of container technologies mentioned in the script?
-Examples of container technologies mentioned include Docker, Cloud Foundry, and Rocket.
What is the smallest size of a node.js VM mentioned in the script?
-The smallest size of a node.js VM mentioned is over 400 megabytes, which is significantly larger than the node.js runtime and application itself, which would be under 15 megabytes.
Why can using VMs create issues when pushing applications into production?
-Using VMs can create issues in production because applications might work on the local machine but encounter incompatibilities when deployed on a VM, which can hinder agile DevOps, continuous integration, and delivery.
What is the three-step process for working with containers?
-The three-step process for working with containers involves creating a manifest (like a Dockerfile), building the image, and then creating the container itself.
What is the role of a Dockerfile in containerization?
-A Dockerfile is a manifest that describes the container, specifying the steps needed to assemble the container image.
How does containerization help with scaling applications?
-Containerization helps with scaling applications by allowing for the deployment of multiple lightweight containers without the need for a guest OS, thus using fewer resources and enabling more efficient scaling.
What is the advantage of container technology when accessing third-party services?
-Container technology allows for modular and cloud-native architectures, enabling the deployment of individual components like a Python application for accessing a third-party service, without the need to bundle them into a single VM.
How do shared resources work in container-based technology?
-In container-based technology, shared resources can be utilized by any container if they are not being used by another process, allowing for efficient resource allocation and utilization.
What does the script suggest about the future lesson's topic?
-The script suggests that the next lesson will cover cloud storage, which is likely to discuss storage solutions that complement containerization and cloud-native applications.
Outlines
💻 Introduction to Containers
The script introduces containers as a method of packaging application code with its libraries and dependencies into an executable unit that can run on any platform, including desktops, traditional IT, and the cloud. Containers are highlighted for their small size, speed, and portability, contrasting with virtual machines that require a guest OS in each instance. Sai Vennam, a developer advocate at IBM, discusses the history of container technology, starting from the Linux kernel's introduction of control groups in 2008. He uses a node.js application as an example to illustrate the advantages of containerization over VMs, such as reduced resource consumption and ease of scaling. Sai also touches on the issues of portability and compatibility that arise with VMs, which containers help to resolve.
🚀 Containerization Process and Benefits
This paragraph delves into the three-step process of containerization: creating a manifest (like a Dockerfile), building the image, and pushing it to a registry to create a container. It contrasts this with the VM approach, emphasizing the lightweight nature of containers that contain only the necessary libraries and application binaries, thus using fewer resources. The script uses a scenario where a node.js application is containerized and scaled, demonstrating how containers allow for efficient use of resources. It also explores the modularity of containers, showing how they can be combined with other services, like a Python application accessing a cognitive API for image recognition. The benefits of containerization for cloud-native architectures, such as portability, scalability, and facilitation of agile DevOps practices, are highlighted.
Mindmap
Keywords
💡Containers
💡Executable unit
💡Virtual Machines (VMs)
💡Portability
💡Linux Kernel
💡C groups
💡Docker
💡Kubernetes
💡Cloud Native
💡DevOps
💡Continuous Integration and Delivery (CI/CD)
Highlights
Containers are an executable unit of software that packages application code with libraries and dependencies.
Containers can run anywhere, including desktop, traditional IT, or the cloud.
They are small, fast, and portable compared to virtual machines.
Containers do not need a guest OS in every instance and can leverage the host OS.
Container technology has been around since 2008 with the introduction of Linux kernel control groups.
Examples of container technologies include Docker, Cloud Foundry, Rocket, and others.
A node.js application example is used to explain the advantages of containerization.
VMs require a guest OS, binaries, and libraries, which can bloat the application size.
The smallest node.js VM is over 400MB, whereas the app itself is under 15MB.
Scaling VMs requires deploying a separate guest OS and libraries for each instance.
Containerization solves the 'it works on my machine' problem.
The three-step process of containerization involves creating a manifest, building the image, and creating the container.
Containers are more lightweight than VMs and use fewer resources.
Container technology allows for better utilization of shared resources.
Containers enable true cloud-native architectures and easier scaling.
Containerization streamlines development and deployment of Cloud Native applications.
The next lesson will cover cloud storage.
Transcripts
Containers are an executable unit of software in which application code is packaged, along
with its libraries and dependencies, in common ways so that it can be run anywhere, whether
it be on desktop, traditional IT, or the cloud.
Containers are small, fast, and portable, and unlike virtual machines, they do not need
to include a guest OS in every instance and can, instead, simply leverage the features
and resources of the host OS.
In the rest of this video, we will see how container-based technology really works.
Hi everyone.
My name is Sai Vennam and I'm a developer advocate with IBM.Today I want to talk about
containerization.
Whenever I mention containers, most people tend to default to something like Docker or
even Kubernetes these days.
But container technology has actually been around for quite some time.
It's actually back in 2008 that the Linux kernel introduced C groups, and control groups,
that basically paved the way for all the different container technologies we see today.
So that includes Docker, but also things like Cloud Foundry, as well as Rocket and other
container runtimes out there.
Let's get started with an example, and we'll say that I was a developer.
I've created a node.js application and I want to push it into production.
We'll take two different form factors to kind of explain the advantages of containerization.
Let's say that first we'll talk about VMs, and then we'll talk about containers.
First things first, let's introduce some of the things that we've got here.
We've got the hardware itself, just a big box.
We've got the guest, or rather, the host, operating system, as well as a hypervisor.
Hypervisor is actually what allows us to spin up VMs.
Let's take a look at this shared pool of resources with the host OS and hypervisor.
We can assume that some of these resources have already been consumed.
Next, let's go ahead and take this .js application and push it in.
And to do that, I need a Linux VM, so let's go ahead and sketch out that Linux VM.
In this VM there's a few things to note here.
We've got another operating system, in addition to the host OS, it's gonna be the guest OS,
as well as some binaries and libraries.
That's one of the things about Linux VMs, that even though we're working with a really
lightweight application, to create that Linux VM, we have to put that guest OS in there,
in a set of binaries and libraries.
That really bloats it out.
In fact, I think the smallest node .js VM that I've seen out there is 400 plus mega
bytes, whereas the node.js runtime and app itself would be under 15.
So we've got that and we'll go ahead and let's push that .js application into it.
Just by doing that alone, we're gonna consume a set of resources.
Next, let's think about scaling this out.
So we'll create two additional copies of it, and you'll notice that even though it's the
exact same application, we have to use and deploy that separate guest OS and libraries
every time.
And so we'll do that three times.
And by doing that, essentially, we can assume that for this particular hardware we've consumed
all of the all of the resources.
There's another thing that I haven't mentioned here, but this .js application, I developed
it on my macbook.
So when I pushed it into production to get it going on the VM, and notice that there
were some issues and incompatibilities.
This is the kind of foundations is big, he said, she said issue.
Where things might be working on your local machine, and work great, but when you try
to push it into production, things start to break.
And this really gets in the way of doing agile DevOps, and continuous integration and delivery.
That's solved when you use something like containers.
There's a three-step process when kind of doing anything container related, and then
pushing, or creating, containers.
And it almost always starts with first, some sort of a manifest.
So something that describes the container itself.
In the Docker world, this would be something like a Docker file.
And in Cloud Foundry, this would be a manifest Channel.
Next, what you'll do is create the actual image itself.
For the image, again, if you're working with something like Docker, that could be something
like a Docker image.
If you're working with Rocket it would be an ACI or application container image.
So regardless of the different containerization technologies, this process stays the same.
The last thing you end up with is an actual container itself.
You know, that contains all of the runtimes, and libraries, and binaries needed to run
an application.
That application runs on a very similar set up to the VMS, but what we've got on this
side is, again, a host operating system.
The difference here, instead of a hypervisor, we're gonna have something like a runtime
engine.
So if you're using Docker this would be the Docker engine and, you know, different containerization
technologies would have a different engine.
Regardless, it's something that runs those containers.
Again, we've got this shared pool of resources, so we can assume that that alone consumes
some set of resources.
Next, let's think about actually containerizing this technology.
We talked about the three-step process.
We create a docker file.
We build out the image.
We push it to a registry, and we have our container and we can start pushing this out
as containers.
The great thing is, these are going to be much more lightweight.
So deploying out multiple containers, since you don't have to worry about a guest OS this
time, you really just have the libraries, as well as the the application itself.
We've scaled that out three times, and because we don't have to duplicate all of those operating
system dependencies and create bloated VMs, we actually will use less resources.
So use a different color here... and scaling that out three times, we still have a good
amount of resources left.
Next, let's say that my coworker decides, hey, for this .js application, let's take
advantage of a third party - let's say a cognitive API - to do something like image recognition.
Let's say that we've got our third party service, and we want to access that using maybe a Python
application.
So he's created that service that acts as that third party APIs.
And with our node.js application, we want to access that Python app, to then access
that service.
If we wanted to do this in VMs, I'm really tempted to basically create a VM out of both
the .js application and the Python application.
Because essentially that would allow me to continue to use the VMs that I have.
But that's not truly cloud native, right?
Because if I wanted to scale out the .js, but not the Python app, I wouldn't be able
to if they were running in the same VM.
So to do it in a truly cloud native way, essentially I would have to free up some of these resources.
Basically get rid of one of these VMs, and then deploy the Python application in it instead.
And you know, that's not ideal.
But with the container based approach what we can do is simply say, since we're modular,
we can say, okay, just deploy one copy of the Python application.
So we'll go ahead and do that.
There's a different color here.
And that consumes a little bit more resources.
Then with those those remaining resources, the great thing about container technology,
that actually becomes shared between all the processes running.
In fact, another advantage if some of these container processes aren't actually utilizing
the CPU or memory, all of those shared resources become accessible for the other containers
running within that hardware.
So with container-based technology, we can truly take advantage of cloud native based
architectures.
We talked about things like portability of the containers.
We talked about how it's easier to scale them out.
And then overall, with this kind of three-step process and the way we push containers, allows
for more agile devops and continuous integration and delivery.
Containers streamline development and deployment of Cloud Native applications.
In the next lesson, we will cover cloud storage.
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