Have you ever think🤔, how companies are using Kubernetes?🧐

What is Kubernetes ? 🙄

Kubernetes is also known as ‘k8s’. This word comes from the Greek language, which means a pilot or helmsman.
Kubernetes is an extensible, portable, and open-source platform designed by Google in 2014. It is mainly used to automate the deployment, scaling, and operations of the container-based applications across the cluster of nodes. It is also designed for managing the services of containerized apps using different methods which provide the scalability, predictability, and high availability.

It is actually an enhanced version of ‘Borg’ for managing the long-running processes and batch jobs. Nowadays, many cloud services offer a Kubernetes-based infrastructure on which it can be deployed as the platform-providing service. This technique or concept works with many container tools, like docker, and follows the client-server architecture.

Why use Kubernetes?

Here, are pros/benefits of using Kubernetes.

• Kubernetes can run on-premises bare metal, OpenStack, public clouds Google, Azure, AWS, etc.
• Helps you to avoid vendor lock issues as it can use any vendor-specific APIs or services except where Kubernetes provides an abstraction, e.g., load balancer and storage.
• Containerization using kubernetes allows package software to serve these goals. It will enable applications that need to be released and updated without any downtime.
• Kubernetes allows you to assure those containerized applications run where and when you want and helps you to find resources and tools which you want to work.

Benefits of Kubernetes for companies:

• Control and automate deployments and updates
• Save money by optimizing infrastructural resources thanks to the more efficient use of hardware
• Orchestrate containers on multiple hosts
• Solve many common problems deriving by the proliferation of containers by organizing them in “pods” (see the last post!)
• Scale resources and applications in real time
• Test and autocorrection of applications

Features of Kubernetes:-

Pod: It is a deployment unit in Kubernetes with a single Internet protocol address.

Horizontal Scaling: It is an important feature in the Kubernetes. This feature uses a Horizontal Pod Autoscalar to automatically increase or decrease the number of pods in a deployment, replication controller, replica set, or stately set on the basis of observed CPU utilization.

Automatic Bin Packing: Kubernetes helps the user to declare the maximum and minimum resources of computers for their containers.

Service Discovery and load balancing: Kubernetes assigns the IP addresses and a Name of DNS for a set of containers, and also balances the load across them.

Automated rollouts and rollbacks: Using the rollouts, Kubernetes distributes the changes and updates to an application or its configuration. If any problem occurs in the system, then this technique rollbacks those changes for you immediately.

Persistent Storage: Kubernetes provides an essential feature called ‘persistent storage’ for storing the data, which cannot be lost after the pod is killed or rescheduled. Kubernetes supports various storage systems for storing the data, such as Google Compute Engine’s Persistent Disks (GCE PD) or Amazon Elastic Block Storage (EBS). It also provides the distributed file systems: NFS or GFS.

Self-Healing: This feature plays an important role in the concept of Kubernetes. Those containers which are failed during the execution process, Kubernetes restarts them automatically. And, those containers which do not reply to the user-defined health check, it stops them from working automatically.

What is Kubernetes used for?

Kubernetes is used to create applications that are easy to manage and deploy anywhere. When available as a managed service, Kubernetes offers you a range of solutions to meet your needs. Here are some common use cases.

Increasing development velocity

Kubernetes helps you to build cloud-native microservices-based apps. It also supports containerization of existing apps, thereby becoming the foundation of application modernization and letting you develop apps faster.

Deploying applications anywhere

Kubernetes is built to be used anywhere, allowing you to run your applications across on-site deployments and public clouds; as well as hybrid deployments in between. So you can run your applications where you need them.

Running efficient services

Kubernetes can automatically adjust the size of a cluster required to run a service. This enables you to automatically scale your applications, up and down, based on the demand and run them efficiently.

CASE STUDY: Adidas

Staying True to Its Culture, adidas Got 40% of Its Most Impactful Systems Running on Kubernetes in a Year

Challenge

In recent years, the adidas team was happy with its software choices from a technology perspective — but accessing all of the tools was a problem. For instance, “just to get a developer VM, you had to send a request form, give the purpose, give the title of the project, who’s responsible, give the internal cost center a call so that they can do recharges,” says Daniel Eichten, Senior Director of Platform Engineering. “The best case is you got your machine in half an hour. Worst case is half a week or sometimes even a week.”

Solution

To improve the process, “we started from the developer point of view,” and looked for ways to shorten the time it took to get a project up and running and into the adidas infrastructure, says Senior Director of Platform Engineering Fernando Cornago. They found the solution with containerization, agile development, continuous delivery, and a cloud native platform that includes Kubernetes and Prometheus.

Impact

Just six months after the project began, 100% of the adidas e-commerce site was running on Kubernetes. Load time for the e-commerce site was reduced by half. Releases went from every 4–6 weeks to 3–4 times a day. With 4,000 pods, 200 nodes, and 80,000 builds per month, adidas is now running 40% of its most critical, impactful systems on its cloud native platform.

Go through this link for more info. :- adidas

CASE STUDY: Babylon

AppDirect : How Cloud Native Is Enabling Babylon’s Medical AI Innovations

Challenge

A large number of Babylon’s products leverage machine learning and artificial intelligence, and in 2019, there wasn’t enough computing power in-house to run a particular experiment. The company was also growing (from 100 to 1,600 in three years) and planning expansion into other countries.

Solution

Babylon had migrated its user-facing applications to a Kubernetes platform in 2018, so the infrastructure team turned to Kubeflow, a toolkit for machine learning on Kubernetes. “We tried to create a Kubernetes core server, we deployed Kubeflow, and we orchestrated the whole experiment, which ended up being a really good success,” says AI Infrastructure Lead Jérémie Vallée. The team began building a self-service AI training platform on top of Kubernetes.

Impact

Instead of waiting hours or days to be able to compute, teams can get access instantaneously. Clinical validations used to take 10 hours; now they are done in under 20 minutes. The portability of the cloud native platform has also enabled Babylon to expand into other countries.

Go through this link for more info. :- babylon

CASE STUDY: VSCO

VSCO: How a Mobile App Saved 70% on Its EC2 Bill with Cloud Native

Challenge

After moving from Rackspace to AWS in 2015, VSCO began building Node.js and Go microservices in addition to running its PHP monolith. The team containerized the microservices using Docker, but “they were all in separate groups of EC2 instances that were dedicated per service,” says Melinda Lu, Engineering Manager for the Machine Learning Team. Adds Naveen Gattu, Senior Software Engineer on the Community Team: “That yielded a lot of wasted resources. We started looking for a way to consolidate and be more efficient in the AWS EC2 instances.”

Solution

The team began exploring the idea of a scheduling system, and looked at several solutions including Mesos and Swarm before deciding to go with Kubernetes. VSCO also uses gRPC and Envoy in their cloud native stack.

Impact

Before, deployments required “a lot of manual tweaking, in-house scripting that we wrote, and because of our disparate EC2 instances, Operations had to babysit the whole thing from start to finish,” says Senior Software Engineer Brendan Ryan. “We didn’t really have a story around testing in a methodical way, and using reusable containers or builds in a standardized way.” There’s a faster onboarding process now. Before, the time to first deploy was two days’ hands-on setup time; now it’s two hours. By moving to continuous integration, containerization, and Kubernetes, velocity was increased dramatically. The time from code-complete to deployment in production on real infrastructure went from one to two weeks to two to four hours for a typical service. Adds Gattu: “In man hours, that’s one person versus a developer and a DevOps individual at the same time.” With an 80% decrease in time for a single deployment to happen in production, the number of deployments has increased as well, from 1200/year to 3200/year. There have been real dollar savings too: With Kubernetes, VSCO is running at 2x to 20x greater EC2 efficiency, depending on the service, adding up to about 70% overall savings on the company’s EC2 bill. Ryan points to the company’s ability to go from managing one large monolithic application to 50+ microservices with “the same size developer team, more or less. And we’ve only been able to do that because we have increased trust in our tooling and a lot more flexibility, so we don’t need to employ a DevOps engineer to tune every service.” With Kubernetes, gRPC, and Envoy in place, VSCO has seen an 88% reduction in total minutes of outage time, mainly due to the elimination of JSON-schema errors and service-specific infrastructure provisioning errors, and an increased speed in fixing outages.

Go through this link for more info. :- vsco

CASE STUDY: Spotify

Spotify: An Early Adopter of Containers, Spotify Is Migrating from Homegrown Orchestration to Kubernetes

Challenge

Launched in 2008, the audio-streaming platform has grown to over 200 million monthly active users across the world. “Our goal is to empower creators and enable a really immersive listening experience for all of the consumers that we have today — and hopefully the consumers we’ll have in the future,” says Jai Chakrabarti, Director of Engineering, Infrastructure and Operations. An early adopter of microservices and Docker, Spotify had containerized microservices running across its fleet of VMs with a homegrown container orchestration system called Helios. By late 2017, it became clear that “having a small team working on the features was just not as efficient as adopting something that was supported by a much bigger community,” he says.

Solution

“We saw the amazing community that had grown up around Kubernetes, and we wanted to be part of that,” says Chakrabarti. Kubernetes was more feature-rich than Helios. Plus, “we wanted to benefit from added velocity and reduced cost, and also align with the rest of the industry on best practices and tools.” At the same time, the team wanted to contribute its expertise and influence in the flourishing Kubernetes community. The migration, which would happen in parallel with Helios running, could go smoothly because “Kubernetes fit very nicely as a complement and now as a replacement to Helios,” says Chakrabarti.

Impact

The team spent much of 2018 addressing the core technology issues required for a migration, which started late that year and is a big focus for 2019. “A small percentage of our fleet has been migrated to Kubernetes, and some of the things that we’ve heard from our internal teams are that they have less of a need to focus on manual capacity provisioning and more time to focus on delivering features for Spotify,” says Chakrabarti. The biggest service currently running on Kubernetes takes about 10 million requests per second as an aggregate service and benefits greatly from autoscaling, says Site Reliability Engineer James Wen. Plus, he adds, “Before, teams would have to wait for an hour to create a new service and get an operational host to run it in production, but with Kubernetes, they can do that on the order of seconds and minutes.” In addition, with Kubernetes’s bin-packing and multi-tenancy capabilities, CPU utilization has improved on average two- to threefold.

Go through this link for more info. :- spotify

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