Kubernetes is a big open source project with a lot of code and a lot of functionality. You have probably read about Kubernetes, and maybe even dipped your toes in and used it in a side project or maybe even at work. But to understand what Kubernetes is all about, how to use it effectively, and what the best practices are requires much more. In this chapter, we will build together the foundation necessary to utilize Kubernetes to its full potential. We will start by understanding what container orchestration means. Then we will cover important Kubernetes concepts that will form the vocabulary we will use throughout the book.
After that, we will dive into the architecture of Kubernetes proper and look at how it enables all the capabilities Kubernetes provides to its users. Then, we will discuss the various runtimes and container engines that Kubernetes supports (Docker is just one option), and finally, we will discuss the role of Kubernetes in the full continuous integration and deployment pipeline.
At the end of this chapter, you will have a solid understanding of container orchestration, what problems Kubernetes addresses, the rationale for Kubernetes design and architecture, and the different runtime it supports. You'll also be familiar with the overall structure of the open source repository and be ready to jump in and find answers to any question.
Understanding container orchestration
The primary responsibility of Kubernetes is container orchestration. That means making sure that all the containers that execute various workloads are scheduled to run physical or virtual machines. The containers must be packed efficiently following the constraints of the deployment environment and the cluster configuration. In addition, Kubernetes must keep an eye on all running containers and replace dead, unresponsive, or otherwise unhealthy containers. Kubernetes provides many more capabilities that you will learn about in the following chapters. In this section, the focus is on containers and their orchestration.
Physical machines, virtual machines, and containers
It all starts and ends with hardware. In order to run your workloads, you need some real hardware provisioned. That includes actual physical machines, with certain compute capabilities (CPUs or cores), memory, and some local persistent storage (spinning disks or SSDs). In addition, you will need some shared persistent storage and to hook up all these machines using networking so they can find and talk to each other. At this point, you run multiple virtual machines on the physical machines or stay at the bare-metal level (no virtual machines). Kubernetes can be deployed on a bare-metal cluster (real hardware) or on a cluster of virtual machines. Kubernetes in turn can orchestrate the containers it manages directly on bare metal or on virtual machines. In theory, a Kubernetes cluster can be composed of a mix of bare-metal and virtual machines, but this is not very common.
Containers in the cloud
Containers are ideal to package microservices because, while providing isolation to the microservice, they are very lightweight and you don't incur a lot of overhead when deploying many microservices as you do with virtual machines. That makes containers ideal for cloud deployment, where allocating a whole virtual machine for each microservice would be cost prohibitive.
All major cloud providers such as AWS, GCE, and Azure provide container hosting services these days. Some of them, such as Google's GKE, are based on Kubernetes. Others, such as Microsoft Azure's container service, are based on other solutions (Apache Mesos). By the way, AWS has the ECS (containers service over EC2) using their own orchestration solution. The great thing about Kubernetes is that it can be deployed on all those clouds. Kubernetes has a cloud provider interface that allows any cloud provider to implement it and integrate Kubernetes seamlessly.
Cattle versus pets
In the olden days, when systems were small, each server had a name. Developers and users knew exactly what software was running on each machine. I remember that, in many of the companies I worked for, we had multi-day discussions to decide on a naming theme for our servers. For example, composers and Greek mythology characters were popular choices. Everything was very cozy. You treated your servers like beloved pets. When a server died it was a major crisis. Everybody scrambled to try to figure out where to get another server, what was even running on the dead server, and how to get it working on the new server. If the server stored some important data, then hopefully, you had an up-to-date backup and maybe you'd even be able to recover it.
Obviously, that approach doesn't scale. When you have a few tens or hundreds of servers, you must start treating them like cattle. You think about the collective and not individuals. You may still have some pets (that is, your build machines), but your web servers are just cattle.
Kubernetes takes the cattle approach to the extreme and takes full responsibility for allocating containers to specific machines. You don't need to interact with individual machines (nodes) most of the time. This works best for stateless workloads. For stateful applications, the situation is a little different, but Kubernetes provides a solution called PetSet, which we'll discuss soon.
In this section, we covered the idea of container orchestration and discussed the relationships between hosts (physical or virtual) and containers, as well as the benefits of running containers in the cloud, and finished with a discussion about cattle versus pets. In the following section, we will get to know the world of Kubernetes and learn its concepts and terminology.
In this section, I'll briefly introduce many important Kubernetes concepts and give you some context as to why they are needed and how they interact with other concepts. The goal is to get familiar with these terms and concepts. Later, we will see how these concepts are woven together to achieve awesomeness. You can consider many of these concepts as building blocks. Some of the concepts, such as node and master, are implemented as a set of Kubernetes components. These components are at a different abstraction level, and I discuss them in detail in a dedicated section, Kubernetes components.
A cluster is a collection of hosts storage and networking resources that Kubernetes uses to run the various workloads that comprise your system. Note that your entire system may consist of multiple clusters. We will discuss this advanced use case of federation in detail later.
A node is a single host. It may be a physical or virtual machine. Its job is to run pods. Each Kubernetes node runs several Kubernetes components, such as a kubelet and a kube proxy. Nodes are managed by a Kubernetes master. The nodes are worker bees of Kubernetes and shoulder all the heavy lifting. In the past they were called minions. If you read some old documentation or articles, don't get confused. Minions are nodes.
The master is the control plane of Kubernetes. It consists of several components, such as an API server, a scheduler, and a controller manager. The master is responsible for the global, cluster-level scheduling of pods and handling of events. Usually, all the master components are set up on a single host. When considering high-availability scenarios or very large clusters, you will want to have master redundancy. I will discuss highly available clusters in detail in Chapter 4, Kubernetes High Availability and Scaling.
A pod is the unit of work in Kubernetes. Each pod contains one or more containers. Pods are always scheduled together (always run on the same machine). All the containers in a pod have the same IP address and port space; they can communicate using localhost or standard inter-process communication. In addition, all the containers in a pod can have access to shared local storage on the node hosting the pod. The shared storage will be mounted on each container. Pods are important feature of Kubernetes. It is possible to run multiple applications inside a single Docker container by having something like supervisord as the main Docker application that runs multiple processes, but this practice is often frowned upon, for the following reasons:
- Transparency: Making the containers within the pod visible to the infrastructure enables the infrastructure to provide services to those containers, such as process management and resource monitoring. This facilitates a number of conveniences for users.
- Decoupling software dependencies: The individual containers may be versioned, rebuilt, and redeployed independently. Kubernetes may even support live updates of individual containers someday.
- Ease of use: Users don't need to run their own process managers, worry about signal and exit-code propagation, and so on.
- Efficiency: Because the infrastructure takes on more responsibility, containers can be more lightweight.
Pods provide a great solution for managing groups of closely related containers that depend on each other and need to co-operate on the same host to accomplish their purpose. It's important to remember that pods are considered ephemeral, throwaway entities that can be discarded and replaced at will. Any pod storage is destroyed with its pod. Each pod gets a unique ID (uid), so you can still distinguish between them if necessary.
Labels are key-value pairs that are used to group together sets of objects, very often pods. This is important for several other concepts, such as replication controller, replica sets, and services that operate on dynamic groups of objects and need to identify the members of the group. There is a NxN relationship between objects and labels. Each object may have multiple labels, and each label may be applied to different objects. There are certain restrictions by design on labels. Each label on an object must have a unique key. The label key must adhere to a strict syntax. It has two parts: prefix and name. The prefix is optional. If it exists then it is separated from the name by a forward slash (/) and it must be a valid DNS sub-domain. The prefix must be 253 characters long at most. The name is mandatory and must be 63 characters long at most. Names must start and end with an alphanumeric character (a-z,A-Z,0-9) and contain only alphanumeric characters, dots, dashes, and underscores. Values follow the same restrictions as names. Note that labels are dedicated for identifying objects and NOT for attaching arbitrary metadata to objects. This is what annotations are for (see the following section).
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