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Table of Contents
This article will introduce Kubernetes, so you know why it exists, what it is, and how to use it. We’ll look at some examples of how Kubernetes can be used to power data science workloads.
To learn Kubernetes, Our Kubernetes Application Developer (CKAD) Study Guide would be the best fit for you.
Kubernetes is a sophisticated open-source framework for managing containerized applications in a clustered environment that Google created. Its goal is to simplify the management of connected, dispersed components and services across a wide range of infrastructure.
As a Kubernetes user, you can specify how your applications should function and how they should communicate with other apps and the rest of the world. To test features or rollback complex deployments, you can scale your services up or down, make smooth-rolling updates, and switch traffic between multiple versions of your apps. Kubernetes provides interfaces and composable platform primitives for defining and managing applications with high flexibility, power, and dependability.
It is helpful to understand how Kubernetes is structured and organized at a high level to understand how it can deliver these capabilities. Kubernetes can be thought of as a layer-based system, with each higher layer abstracting the lower ones’ complexity.
Kubernetes is a container orchestration system that groups real or virtual machines into a cluster and uses a shared network to communicate between them. On the physical platform of this cluster, all Kubernetes components, capabilities, and workloads are configured. Within the Kubernetes ecosystem, each cluster’s machine is assigned a role. The controller server is one server (or a few servers). This server serves as the cluster’s gateway and brain, providing an API for users and clients, monitoring the health of other servers, deciding how best to divide and allocate work (known as “scheduling”), and organizing communication between the various components. The master server is the cluster’s main point of contact, and it is in charge of the majority of Kubernetes’ centralized logic.
The cluster’s other computers are called nodes, which are servers that accept and perform workloads using local and external resources. Kubernetes runs applications and services in containers to help with isolation, management, and flexibility. Therefore, each node must have a container runtime installed.
The underlying components ensure that the applications’ desired state corresponds to the cluster’s actual state. Users communicate with the cluster directly or through clients and libraries by interacting with the main API server. A declarative plan in JSON or YAML detailing what to construct and how it should be managed is submitted to start up an application or service. The master server then examines the requirements and the present state of the system to determine how to run the plan on the infrastructure. Kubernetes’ last layer is a collection of user-defined applications that execute according to rules.
The master server, as previously stated, serves as the primary control plane for Kubernetes clusters. It is the primary point of contact for administrators and users and provides several cluster-wide systems for the less-sophisticated worker nodes. The master server’s components collaborate to accept user requests, decide the optimal ways to schedule workload containers, authenticate clients and nodes, change cluster-wide networking, and manage scaling and health checking tasks.
These components can be placed on a single server or distributed across several. In this section, we will look at each of the different components that make up master servers.
A globally available configuration store is one of the essential components for Kubernetes to work.
Kubernetes stores configuration data in etcd, accessible to all cluster nodes. This can be used to discover services and assist components in configuring or reconfiguring themselves based on current information. It also aids in cluster state maintenance with leader election and distributed locking features.
An API server is one of the most effective master services. The cluster’s primary management interface lets users configure Kubernetes workloads and organizational units. It is also in charge of ensuring that the etcd store and the service information of deployed containers match. It serves as a link between various components to keep the cluster healthy and distribute data and orders.
The controller manager is a generic service with a wide range of duties. It primarily oversees many controllers that regulate the cluster’s state, manages workload life cycles, and carry out basic activities. A replication controller, for example, makes sure that the number of replicas (identical copies) defined for a pod corresponds to the number of replicas currently deployed on the cluster. The controller manager checks for changes through the API server, and the details of these operations are written to etcd.
The scheduler is the process that assigns workloads to specific nodes in the cluster. This service takes the operational requirements of a workload, assesses the current infrastructure environment, and assigns the work to a suitable node or nodes.
Kubernetes may be used in various scenarios and can communicate with various infrastructure providers to understand and manage the cluster’s resources. While Kubernetes works with general representations of resources like attachable storage and load balancers, it requires a means to link these representations to the actual resources given by non-homogeneous cloud providers.
Nodes in Kubernetes are servers that perform work by running containers. Node servers must meet a few requirements to communicate with master components, configure container networking, and operate the workloads assigned to them.
The container runtime is in charge of starting and managing containers. It is the component on each node that finally runs the containers defined in the workloads provided to the cluster.
A simple service named kubelet serves as the main point of contact for each node with the cluster group. This service is in charge of conveying data to and from the control plane services and communicating with the etcd store to read or write new values.
A small proxy service known as Kube-proxy is run on each node server to manage individual host subnetting and make services available to other components. This process routes requests to the appropriate containers, perform essential load balancing and ensures that the networking environment is stable and available.
Kubernetes works with pods, which are the most fundamental units. Containers do not have hosts assigned to them. Instead, one or more tightly connected containers are encased in a pod-like device.
Containers in a pod collaborate closely, have the same life cycle, and should be scheduled on the same node. They are all administered as single entities, with the same environment, volumes, and IP space.
A replication controller is a type of object that defines a pod template and controls the settings for horizontally scaling identical clones of a pod by changing the number of running copies. Within Kubernetes, this is a simple approach to disperse load and boost availability because a template that closely matches a pod specification is contained in the replication controller configuration. The replication controller knows how to construct additional pods as needed.
Replication sets are an evolution of the replication controller concept that gives the controller more freedom to identify the pods it is supposed to handle. Replication sets are beginning to supplant replication controllers due to their superior replica selection capabilities; however, unlike replication controllers, they cannot perform rolling upgrades to cycle backends to a new version.
One of the most typical workloads to directly build and manage is deployments. Replication sets are used as a building component in deployments. While deployments constructed with replication sets may appear to mimic the functionality provided by replication controllers, they address many of the issues that plagued rolling update implementation. Users must submit a proposal for a new replication controller to replace the current controller when updating applications with replication controllers. Tasks like tracking history, recovering from network outages during the update, and rolling back bad changes are either difficult or left to the user’s discretion when utilizing replication controllers.
Stateful sets are specialized pod controllers with assurances of order and uniqueness. These are mainly utilized to provide more fine-grained management when unique needs such as deployment ordering, permanent data, or steady networking. Stateful sets, for example, are frequently linked with data-oriented applications, such as databases, which require access to the same volumes regardless of whether they are rescheduled to a new node.
Another type of pod controller is daemon sets, which run a copy of a pod on each node in the cluster. This is especially handy when deploying pods to assist with maintenance and services for the nodes themselves.
Kubernetes employs a workload known as jobs to create a more task-based workflow in which running containers are expected to exit successfully after completing their tasks after a certain amount of time.
The following are the fundamental tools you should be familiar with:
It is intended to be a simple way for new users to create clusters.
It is a tool that allows you to interact with your existing cluster.
It is a tool that makes it simple to run Kubernetes on a local machine. HomeBrew simplifies the use of Minikube for Mac users.
Kubernetes is an exciting project that allows users to run containerized workloads that are scalable and highly available on a highly abstracted platform. While the architecture and set of internal components of Kubernetes may appear intimidating at first, their power, flexibility, and robust feature set are unparalleled in the open-source world. Understanding how the basic building blocks fit together allows you to start designing systems that fully utilize the platform’s capabilities for running and managing your workloads at scale.
By 2024, 70% of new applications developed with programming languages will be deployed in containers worldwide for improved deployment speed, application consistency, and portability.
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