KUBERNETES: how Kubernetes is used in Industries and what all use cases are solved by Kubernetes?
Kubernetes is a portable, extensible, open-source platform for managing containerized workloads and services, that facilitates both declarative configuration and automation. It has a large, rapidly growing ecosystem. Kubernetes services, support, and tools are widely available.
Let’s take a look at why Kubernetes is so useful by going back in time
Traditional deployment era: Early on, organizations ran applications on physical servers. There was no way to define resource boundaries for applications in a physical server, and this caused resource allocation issues. For example, if multiple applications run on a physical server, there can be instances where one application would take up most of the resources, and as a result, the other applications would underperform. A solution for this would be to run each application on a different physical server. But this did not scale as resources were underutilized, and it was expensive for organizations to maintain many physical servers.
Virtualized deployment era: As a solution, virtualization was introduced. It allows you to run multiple Virtual Machines (VMs) on a single physical server’s CPU. Virtualization allows applications to be isolated between VMs and provides a level of security as the information of one application cannot be freely accessed by another application.
Virtualization allows better utilization of resources in a physical server and allows better scalability because an application can be added or updated easily, reduces hardware costs, and much more. With virtualization you can present a set of physical resources as a cluster of disposable virtual machines.
Each VM is a full machine running all the components, including its own operating system, on top of the virtualized hardware.
Container deployment era: Containers are similar to VMs, but they have relaxed isolation properties to share the Operating System (OS) among the applications. Therefore, containers are considered lightweight. Similar to a VM, a container has its own filesystem, share of CPU, memory, process space, and more. As they are decoupled from the underlying infrastructure, they are portable across clouds and OS distributions.
Containers have become popular because they provide extra benefits, such as:
- Agile application creation and deployment: increased ease and efficiency of container image creation compared to VM image use.
- Continuous development, integration, and deployment: provides for reliable and frequent container image build and deployment with quick and easy rollbacks (due to image immutability).
- Dev and Ops separation of concerns: create application container images at build/release time rather than deployment time, thereby decoupling applications from infrastructure.
- Observability not only surfaces OS-level information and metrics, but also application health and other signals.
- Environmental consistency across development, testing, and production: Runs the same on a laptop as it does in the cloud.
- Cloud and OS distribution portability: Runs on Ubuntu, RHEL, CoreOS, on-premises, on major public clouds, and anywhere else.
- Application-centric management: Raises the level of abstraction from running an OS on virtual hardware to running an application on an OS using logical resources.
- Loosely coupled, distributed, elastic, liberated micro-services: applications are broken into smaller, independent pieces and can be deployed and managed dynamically — not a monolithic stack running on one big single-purpose machine.
- Resource isolation: predictable application performance.
- Resource utilization: high efficiency and density.
Why you need Kubernetes and what it can do
Containers are a good way to bundle and run your applications. In a production environment, you need to manage the containers that run the applications and ensure that there is no downtime. For example, if a container goes down, another container needs to start. Wouldn’t it be easier if this behavior was handled by a system?
That’s how Kubernetes comes to the rescue! Kubernetes provides you with a framework to run distributed systems resiliently. It takes care of scaling and failover for your application, provides deployment patterns, and more. For example, Kubernetes can easily manage a canary deployment for your system.
Kubernetes provides you with:
- Service discovery and load balancing Kubernetes can expose a container using the DNS name or using their own IP address. If traffic to a container is high, Kubernetes is able to load balance and distribute the network traffic so that the deployment is stable.
- Storage orchestration Kubernetes allows you to automatically mount a storage system of your choice, such as local storages, public cloud providers, and more.
- Automated rollouts and rollbacks You can describe the desired state for your deployed containers using Kubernetes, and it can change the actual state to the desired state at a controlled rate. For example, you can automate Kubernetes to create new containers for your deployment, remove existing containers and adopt all their resources to the new container.
- Automatic bin packing You provide Kubernetes with a cluster of nodes that it can use to run containerized tasks. You tell Kubernetes how much CPU and memory (RAM) each container needs. Kubernetes can fit containers onto your nodes to make the best use of your resources.
- Self-healing Kubernetes restarts containers that fail, replaces containers, kills containers that don’t respond to your user-defined health check, and doesn’t advertise them to clients until they are ready to serve.
- Secret and configuration management Kubernetes lets you store and manage sensitive information, such as passwords, OAuth tokens, and SSH keys. You can deploy and update secrets and application configuration without rebuilding your container images, and without exposing secrets in your stack configuration.
Use Cases
We have selected some common use cases to demonstrate Kubernetes’ capabilities. The use cases can be utilized together for different setups.
Self-Healing and Scaling Services
For simplicity, K8s process units can be detailed as pods and services. A pod is the smaller deployment unit available on Kubernetes. A pod can contain several containers that will have some related communication — such as network and storage. Services are the interface that provides accessibility to a set of containers. These services can be for internal or public access and can load balance several container instances.
Pods are mortal: once finished, they vanish from the cluster. Pod termination can be natural or through an error. A deployment is the most modern Kubernetes module to create and maintain pods. Using a single description file, a developer can specify everything necessary to deploy, keep running, scale, and upgrade the pod.
The figure below shows a simple deployment. This creates a pod of Nginx (version 1.7.9) with three replicas. In other words, Kubernetes will manage three Nginx instances; when an instance stops working, Kubernetes will create a new one.
A Kubernetes Deployment of Nginx
This Deployment can be configured to be auto-scalable with the following command line:
1
$ kubectl autoscale deployment nginx-deployment — min=10 — max=15 — cpu-percent=80
One of the advantages of K8s is that it’s easy to understand what the platform is doing. In this case, the cluster will have 10 Nginx instances, and as many as 15 instances if the CPU utilization exceeds 80 percent of capacity.
Serverless, with Server
Serverless architecture has taken the world by a storm since AWS launched Lambda. The principle is simple: just develop the code, and don’t worry about anything else. Server and scalability are handled by the cloud provider and code just has to be developed as functions that handle specific events: from HTTP requests to queue messages.
Vendor lock-in is the major disadvantage of this solution. It almost impossible to change cloud providers without refactoring most of the code. There are some solutions like Serverless that seek to standardize function code across clouds. Another solution is to use a Kubernetes cluster to create a vendor-free serverless platform. As mentioned above, K8S abstracts away the difference between cloud servers. Currently, two popular frameworks virtualize the cluster as a serverless platform: Kubeless and Fission.
Optimized Resource Usage with Namespaces
A K8s namespace is also known as a virtual cluster. Namespaces create a virtually separated cluster inside the real cluster. Clusters without namespaces probably have test, staging and production clusters. Virtual clusters usually waste some resources because they do not undergo continuous testing, and because staging is used from time to time to validate the work of a new feature. By using a virtual cluster, or a namespace, an operations team can use the same set of physical machines for different sets depending on a given workload.
Namespaces are closely related to DNS because services located within the same namespace are accessible through their names. Namespaces offer a good solution for creating similar environments that locate services through network names: instances from different namespaces will find their dependencies without having to take into account which namespace they are located in.
In addition, namespaces can have resource quotas: each virtual cluster can receive a defined allocation in order to avoid a resource competition between namespaces. This is particularly useful to avoid a production environment sharing computing resources with just a few priority environments. Finally, different permissions can be created with roles for each namespace in order to limit the number of individuals with access to production environments.
Hybrid and Multiclouds
A hybrid cloud utilizes computing resources from a local, conventional data center, and from a cloud provider. A hybrid cloud is normally used when a company has some servers in an on-premise data center and wants to use the cloud’s unlimited computing resources to expand or substitute company resources. A multicloud, on the other hand, refers to a cloud that uses multiple cloud providers to handle computing resources. Multiclouds are generally used to avoid vendor lock-in, and to reduce the risk from a cloud provider going down while performing mission-critical operations.
Both solutions are addressed by Kubernetes Federation. Multiple clusters — one for each cloud or on-premise data center — are created that are managed by the Federation. The Federation synchronizes computing resources, and even allows cross-cluster discovery: virtually any pod can communicate with a pod in another cluster without knowing the infrastructure.
The Federation setup is not simple, and there is a caveat: for obvious reasons, the solution doesn’t work on managed services like Google Kubernetes Engine, Azure Container Service or AWS EKS.
Kubernetes Use Cases
Tinder’s Move to Kubernetes
Due to high traffic volume, Tinder’s engineering team faced challenges of scale and stability. What did they do?
The answer is, of course, Kubernetes.
Tinder’s engineering team solved interesting challenges to migrate 200 services and run a Kubernetes cluster at scale totaling 1,000 nodes, 15,000 pods, and 48,000 running containers.
Was that easy? No way. However, they had to do it for the smooth business operations going further. One of their engineering leaders said, “As we onboarded more and more services to Kubernetes, we found ourselves running a DNS service that was answering 250,000 requests per second.” Tinder’s entire engineering organization now has knowledge and experience on how to containerize and deploy their applications on Kubernetes.
Reddit’s Kubernetes Story
Reddit is one of the busiest sites in the world. Kubernetes forms the core of Reddit’s internal infrastructure.
From many years, the Reddit infrastructure team followed traditional ways of provisioning and configuring. However, this didn’t go far until they saw some huge drawbacks and failures happening while doing the things the old way. They moved to Kubernetes.
Airbnb’s Kubernetes Story
Airbnb’s transition from a monolithic to a microservices architecture is pretty amazing. They needed to scale continuous delivery horizontally, and the goal was to make continuous delivery available to the company’s 1,000 or so engineers so they could add new services. Airbnb adopted Kubernetes to support over 1,000 engineers concurrently configuring and deploying over 250 critical services to Kubernetes (at a frequency of about 500 deploys per day on average).