Jump right into Kubernetes development with career-focused lessons and hands-on practice.

DevOps for Developers

Kubernetes is a powerful container management tool that's taking the world by storm. This detailed course will help you master it.

In this course, you'll start with the fundamentals of Kubernetes and what the main components of a cluster look like. You'll then learn how to use those components to build, test, deploy, and upgrade applications, as well as how to achieve state persistence once your application is deployed. You'll also learn how to secure your deployments and manage resources, which are crucial DevOps skills.

By the time you're done, you'll have a firm grasp on what Kubernetes is, as well as the skills to deploy your own clusters and applications with confidence.

A Practical Guide to Kubernetes

# The beginning 
A long time ago in a galaxy far, far away...

We would order servers and wait for months until they arrived. To make our misery worse, even after they came, we'd wait for weeks (sometimes even months) until they were placed in racks and provisioned. 

At that time, only a select few people could access these servers. If someone did something that should not have been done, we would face
an extended downtime. On top of that, nobody knew what was running on those servers. 

Manual provisioning and installations were a nightmare because even after putting a lot of effort into documentation, given enough time, the state of the servers would always diverge from the documentation. Sysadmins were the key people who could handle these servers. 
#  Configuration management and relevant tools 


Managing the configuration means tracking and controlling changes in the software. Configuration management tools enable us to determine what was changed, who changed it and much more.

Then came configuration management tools like **CFEngine**.

## Pros

CFEngine was based on promise theory and was capable of putting a server into the desired state no matter what its actual state was. 

 It allowed us to specify the state of static infrastructure and have a reasonable guarantee that it will be achieved. 

Another big advantage it provided is the ability to have, more or less, the same setup for different environments. Servers dedicated to testing could be (almost) the same as those assigned to production.

## Cons
Unfortunately, usage of CFEngine and similar tools was not yet widespread. We had to wait for virtual machines before automated configuration management became a norm. However, CFEngine was not designed for virtual machines. it was meant to work with static, bare metal servers. Still, CFEngine was a massive contribution to the industry even though it failed to get widespread adoption.

After CFEngine, other similar tools like Chef, Puppet, Ansible, Salt, and more were introduced. We'll discuss these tools later on. For now, let's discuss the next evolutionary improvement.

Besides forcing us to be patient, physical servers were a massive waste of resource utilization. They came in predefined sizes and, since waiting time was considerable, we often opted for big ones. The bigger, the better. That meant that an application or a service usually required less CPU and memory than the server offered. Unless you do not care about costs, that meant that we'd deploy multiple applications to a single server. The result was a dependencies nightmare. We had to choose between freedom and standardization.

Freedom meant that different applications could use different runtime dependencies while standardization involved systems architects deciding the only right way to develop and deploy
something.

# Virtual machines 

Then came the virtual machines (VMs).
Which were a massive improvement over bare metal infrastructure. 
* They allowed us to be more precise with hardware requirements.
* They could be created and destroyed quickly. 

* They could differ, i.e., a single physical server could have multiple VMs running in isolation--one VM could host a Java application, and the other could be dedicated to Ruby on Rails.
* We could get them in a matter of minutes instead of waiting for months--still, it took quite a while to complete the task.

Even though the advantages brought by VMs were numerous, years passed until they were widely adopted. Even then, the adoption was usually wrong. Companies often moved the same practices used with bare metal servers into virtual machines. We could have identical servers in different environments. Companies started copying VMs. While that was much better than before, it did not solve the problem of missing documentation and the ability to create VMs from scratch. Still, multiple identical environments are better than one, even if we don't know what's inside.

## Mutability vs. immutability

 The configuration management tools helped spread the adoption of "infrastructure as code" principles.  But the problem was they were designed with static infrastructure in mind. On the other hand, VMs opened the doors to dynamic infrastructure, in which VMs are continuously created and destroyed. Mutability and constant creation and destruction clashed.

Mutable infrastructure is well suited for static infrastructure. It does not respond well to challenges brought by the dynamic nature of modern data centers. Mutability (changeable at runtime) had to give way to immutability (nothing can be tweaked at runtime).

When ideas behind immutable infrastructure started getting traction, people began combining them with the concepts behind configuration management. However, the tools available at that time were not fit for the job. They (Chef, Puppet, Ansible, and the like) were designed with the idea that servers are brought into the desired state at runtime. On the other hand, immutable processes assume that _almost_ nothing is changeable at runtime. Artifacts were supposed to be created as immutable images. In the case of infrastructure, VMs were created from images, and not changed at runtime. If an upgrade was needed, a new image would be created, followed by a replacement of old VMs with new ones based on the new image. Such processes brought speed and reliability. With proper tests in place, immutable was found to be more reliable than mutable.

Subsequently, we got tools capable of building VM images. Today, they are ruled by Packer. Configuration management tools quickly jumped on board, claiming to work equally well for configuring images as servers, at runtime. However, that was not the case due to the logic behind those tools. They were designed to put a server that is in an unknown state into the desired state, assuming the current state is unknown. VM images, on the other hand, are always based on an image with a known state. If, for example, we choose Ubuntu as a base image, we know what's inside it. 

Adding additional packages and configurations is easy. There is no need for things like "if this, then that, otherwise something else." A simple shell script is as good as any configuration management tool when the current state is known. Creating a VM image is reasonably straightforward with Packer alone. Still, not all was lost for configuration management tools. We could still use them to orchestrate the creation of VMs based on images and potentially do some runtime configuration that couldn't be baked in.

# Cloud hosting
The way we orchestrated infrastructure had to change as well. A higher level of dynamism and elasticity was required. That became especially evident with the emergence of cloud hosting providers like Amazon Web Services (AWS) and, later on, Azure and Google Computer Engine (GCE). 

Learn about the history of infrastructure management. 

A Short History of Infrastructure Management

Learn about the history of infrastructure management.