What Kubernetes is & VSCO Case Study !!

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.

The name Kubernetes originates from Greek, meaning helmsman or pilot. Google open-sourced the Kubernetes project in 2014.

Going back in time

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, the 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 we need Kubernetes and what it can do for us

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 can 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 storage, 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 let 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.

What Kubernetes is not

Kubernetes is not a traditional, all-inclusive PaaS (Platform as a Service) system. Since Kubernetes operates at the container level rather than at the hardware level, it provides some generally applicable features common to PaaS offerings, such as deployment, scaling, load balancing, and lets users integrate their logging, monitoring, and alerting solutions. However, Kubernetes is not monolithic, and these default solutions are optional and pluggable. Kubernetes provides the building blocks for building developer platforms but preserves user choice and flexibility where it is important.

Kubernetes:

• Does not limit the types of applications supported. Kubernetes aims to support an extremely diverse variety of workloads, including stateless, stateful, and data-processing workloads. If an application can run in a container, it should run great on Kubernetes.
• Do not deploy source code and do not build your application. Continuous Integration, Delivery, and Deployment (CI/CD) workflows are determined by organization cultures and preferences as well as technical requirements.
• Does not provide application-level services, such as middleware (for example, message buses), data-processing frameworks (for example, Spark), databases (for example, MySQL), caches, nor cluster storage systems (for example, Ceph) as built-in services. Such components can run on Kubernetes, and/or can be accessed by applications running on Kubernetes through portable mechanisms, such as the Open Service Broker.
• Does not dictate logging, monitoring, or alerting solutions. It provides some integrations as proof of concept, and mechanisms to collect and export metrics.
• Does not provide nor mandate a configuration language/system (for example, Jsonnet). It provides a declarative API that may be targeted by arbitrary forms of declarative specifications.
• Does not provide nor adopt any comprehensive machine configuration, maintenance, management, or self-healing systems.
• Additionally, Kubernetes is not a mere orchestration system. In fact, it eliminates the need for orchestration. The technical definition of orchestration is the execution of a defined workflow: first, do A, then B, then C. In contrast, Kubernetes comprises a set of independent, composable control processes that continuously drive the current state towards the provided desired state. It shouldn't matter how you get from A to C. Centralized control is also not required. This results in a system that is easier to use and more powerful, robust, resilient, and extensible.

Some of the terms of Kubernetes:

Control plane: The collection of processes that control Kubernetes nodes. This is where all task assignments originate.

Nodes: These machines perform the requested tasks assigned by the control plane.

Pod: A group of one or more containers deployed to a single node. All containers in a pod share an IP address, IPC, hostname, and other resources. Pods abstract network and storage from the underlying container. This lets you move containers around the cluster more easily.

Replication controller:  This controls how many identical copies of a pod should be running somewhere on the cluster.

Service: This decouples work definitions from the pods. Kubernetes service proxies automatically get service requests to the right pod—no matter where it moves in the cluster or even if it’s been replaced.

Kubelet: This service runs on nodes, reads the container manifests and ensures the defined containers are started and running.

kubectl: The command-line configuration tool for Kubernetes.

How does Kubernetes work?

A working Kubernetes deployment is called a cluster. You can visualize a Kubernetes cluster as two parts: the control plane and the compute machines, or nodes.

Each node is its own Linux environment and could be either a physical or virtual machine. Each node runs pods, which are made up of containers.

The control plane is responsible for maintaining the desired state of the cluster, such as which applications are running and which container images they use. Compute machines actually run the applications and workloads.

Kubernetes runs on top of an operating system and interacts with pods of containers running on the nodes.

The Kubernetes control plane takes the commands from an administrator (or DevOps team) and relays those instructions to the computing machines.

This handoff works with a multitude of services to automatically decide which node is best suited for the task. It then allocates resources and assigns the pods in that node to fulfill the requested work.

The desired state of a Kubernetes cluster defines which applications or other workloads should be running, along with which images they use, which resources should be made available to them, and other such configuration details.

From an infrastructure point of view, there is little change to how you manage containers. Your control over containers just happens at a higher level, giving you better control without the need to micromanage each separate container or node.

Your work involves configuring Kubernetes and defining nodes, pods, and the containers within them. Kubernetes handles orchestrating the containers.

Where you run Kubernetes is up to you. This can be on bare metal servers, virtual machines, public cloud providers, private clouds, and hybrid cloud environments. One of Kubernetes’ key advantages is it works on many different kinds of infrastructure.

 What about Docker?

Docker can be used as a container runtime that Kubernetes orchestrates. When Kubernetes schedules a pod to a node, the kubelet on that node will instruct Docker to launch the specified containers.

The kubelet then continuously collects the status of those containers from Docker and aggregates that information in the control plane. Docker pulls containers onto that node and starts and stops those containers.

The difference when using Kubernetes with Docker is that an automated system asks Docker to do those things instead of the admin doing so manually on all nodes for all containers. 

CASE STUDY:

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.

After VSCO moved to AWS in 2015 and its user base passed the 30 million mark, the team quickly realized that set-up wouldn't work anymore. Developers had started building some Node and Go microservices, which the team tried containerizing with 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 EC2 instances."

With a checklist that included ease of use and implementation, level of support, and whether it was open source, the team evaluated a few scheduling solutions, including Mesos and Swarm, before deciding to go with Kubernetes. "Kubernetes seemed to have the strongest open-source community around it," says Lu. Plus, "We had started to standardize on a lot of the Google stack, with Go as a language, and gRPC for almost all communication between our own services inside the data center. So it seemed pretty natural for us to choose Kubernetes."

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.

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